diff --git a/.gitattributes b/.gitattributes
deleted file mode 100644
index 06ab1ee..0000000
--- a/.gitattributes
+++ /dev/null
@@ -1,4 +0,0 @@
-*.sh text eol=lf
-*.py text eol=lf
-*.env text eol=lf
-*.yml text eol=lf
diff --git a/.github/workflows/ci-cd-main.yml b/.github/workflows/ci-cd-main.yml
deleted file mode 100644
index e69de29..0000000
diff --git a/.github/workflows/ci-develop.yml b/.github/workflows/ci-develop.yml
index 480f075..9727868 100644
--- a/.github/workflows/ci-develop.yml
+++ b/.github/workflows/ci-develop.yml
@@ -62,33 +62,12 @@ jobs:
 
 
 # Job 2 ─ Full validation (executed only on push events)
-# --------------------------------------------------------------------------- #
-# Includes everything from the quick job plus:
-# • PostgreSQL service container
-# • Alembic migrations
-# • Integration tests
-# • Multi-stage Docker build and health-check
-
   full:
     if: |
       github.event_name == 'push' &&
       github.ref == 'refs/heads/develop'
     runs-on: ubuntu-latest
 
-    services:
-      postgres:
-        image: postgres:17
-        env:
-          POSTGRES_USER: ${{ secrets.DB_USER }}
-          POSTGRES_PASSWORD: ${{ secrets.DB_PASSWORD }}
-          POSTGRES_DB: ${{ secrets.DB_NAME }}
-        ports: ["5432:5432"]
-        options: >-
-          --health-cmd "pg_isready -U $POSTGRES_USER -d $POSTGRES_DB"
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
-
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
@@ -110,41 +89,12 @@ jobs:
       - name: Run mypy
         run: poetry run mypy src
 
-      - name: Apply Alembic migrations
-        env:
-          ENVIRONMENT: test
-          DB_URL: postgresql+psycopg://${{ secrets.DB_USER }}:${{ secrets.DB_PASSWORD }}@localhost:5432/${{ secrets.DB_NAME }}
-        run: poetry run alembic upgrade head
-
       - name: Run all tests
-        env:
-          ENVIRONMENT: test
-          DB_URL: postgresql+asyncpg://${{ secrets.DB_USER }}:${{ secrets.DB_PASSWORD }}@localhost:5432/${{ secrets.DB_NAME }}
         run: |
           poetry run pytest \
             --cov=src --cov-report=term \
             --disable-warnings
 
-      - name: Build Docker image
-        run: docker build --progress=plain -t backend:ci .
+      
 
-      - name: Smoke test container
-        run: |
-          # partiamo con --network host così il container condivide la rete del runner
-          docker run -d \
-            --name backend_ci \
-            --network host \
-            -e ENVIRONMENT=test \
-            -e DB_URL=postgresql+asyncpg://${{ secrets.DB_USER }}:${{ secrets.DB_PASSWORD }}@localhost:5432/${{ secrets.DB_NAME }} \
-            backend:ci \
-            uvicorn app.main:app --host 0.0.0.0 --port 8000
-
-          for i in {1..10}; do
-            if curl --silent --fail http://localhost:8000/health; then
-              echo "✔ Health OK"; break
-            else
-              echo "Waiting…"; sleep 3
-            fi
-          done
-
-          docker stop backend_ci
\ No newline at end of file
+     
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
deleted file mode 100644
index 6bf546c..0000000
--- a/Dockerfile
+++ /dev/null
@@ -1,50 +0,0 @@
-# ─────────────── Build stage ───────────────
-FROM python:3.12-slim AS builder
-
-# Install system dependencies for psycopg and build tools
-RUN apt-get update \
- && apt-get install -y --no-install-recommends gcc libpq-dev curl \
- && rm -rf /var/lib/apt/lists/*
-
-WORKDIR /opt/app
-
-# Copy only pyproject.toml, poetry.lock, README so we leverage cache
-COPY pyproject.toml poetry.lock* README.md ./
-
-# Install Poetry (into /root/.local/bin)
-RUN curl -sSL https://install.python-poetry.org | python3 -
-
-# Symlink Poetry into /usr/local/bin so "poetry" is on PATH
-RUN ln -s /root/.local/bin/poetry /usr/local/bin/poetry
-
-# Tell Poetry not to create its own venv
-RUN poetry config virtualenvs.create false
-
-# Install only the prod deps (uvicorn, fastapi, sqlalchemy, psycopg...)
-RUN poetry install --no-root --without dev
-
-# Now copy in your application code
-COPY src/ ./src
-
-# ─────────── Runtime stage ───────────
-FROM python:3.12-slim AS runtime
-
-WORKDIR /opt/app
-
-# 1) Copy installed libraries
-COPY --from=builder /usr/local/lib/python3.12 /usr/local/lib/python3.12
-
-# 2) Copy console scripts (uvicorn, alembic, etc.)
-COPY --from=builder /usr/local/bin /usr/local/bin
-
-# 3) Copy application code
-COPY --from=builder /opt/app/src ./src
-
-# 4) Non-root user
-RUN adduser --disabled-password --gecos '' appuser
-USER appuser
-
-WORKDIR /opt/app/src
-
-# 5) Default command
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
diff --git a/README.md b/README.md
index a9448e5..526a127 100644
--- a/README.md
+++ b/README.md
@@ -1,176 +1,127 @@
-Certamente. Ecco il contenuto del `README.md` visualizzato direttamente qui.
-
------
-
 # **FastSim Project Overview**
 
 ## **1. Why FastSim?**
 
-FastAPI + Uvicorn gives Python teams a lightning-fast async stack, yet sizing it for production still means guesswork, costly cloud load-tests, or late surprises. **FastSim** fills that gap by becoming a **digital twin** of your actual service:
+Modern async Python stacks like FastAPI + Uvicorn are incredibly fast, yet sizing them for production often involves guesswork, costly cloud load-tests, or late-stage surprises. **FastSim** fills that gap by acting as a **digital twin** of your service:
 
-  * It **replicates** your FastAPI + Uvicorn event-loop behavior in SimPy, generating the same kinds of asynchronous steps (parsing, CPU work, I/O, LLM calls) that happen in real code.
-  * It **models** your infrastructure primitives—CPU cores (via a SimPy `Resource`), database pools, rate-limiters, and even GPU inference quotas—so you can see queue lengths, scheduling delays, resource utilization, and end-to-end latency.
-  * It **outputs** the very metrics you would scrape in production (p50/p95/p99 latency, ready-queue lag, concurrency, throughput, cost per LLM call), but entirely offline, in seconds.
+  * It **replicates** the behavior of an async event-loop in SimPy, generating the same kinds of steps (parsing, CPU work, I/O waits) that happen in real code.
+  * It **models** your infrastructure primitives—CPU cores, connection pools, and rate-limiters—so you can see queue lengths, scheduling delays, resource utilization, and end-to-end latency.
+  * It **outputs** the very metrics you would scrape in production (p50/p95/p99 latency, ready-queue lag, concurrency, throughput), but entirely offline, in seconds.
 
-With FastSim you can ask, *“What happens if traffic doubles on Black Friday?”*, *“How many cores are needed to keep p95 latency below 100 ms?”*, or *“Is our LLM-driven endpoint ready for prime time?”*—and get quantitative answers **before** you deploy.
+With FastSim, you can ask, *“What happens if traffic doubles on Black Friday?”*, *“How many cores are needed to keep p95 latency below 100 ms?”*, or *“Is our new endpoint ready for prime time?”*—and get quantitative answers **before** you deploy.
 
 **Outcome:** Data-driven capacity planning, early performance tuning, and far fewer surprises in production.
 
-## **2. Project Goals**
-
-| \# | Goal | Practical Outcome |
-| :--- | :--- | :--- |
-| 1 | **Pre-production sizing** | Know the required core count, pool size, and replica count to meet your SLA. |
-| 2 | **Scenario analysis** | Explore various traffic models, endpoint mixes, latency distributions, and RTT. |
-| 3 | **Twin metrics** | Produce the same metrics you’ll scrape in production (latency, queue length, CPU utilization). |
-| 4 | **Rapid iteration** | A single YAML/JSON configuration or REST call generates a full performance report. |
-| 5 | **Educational value** | Visualize how GIL contention, queue length, and concurrency react to load. |
-
-## **3. Who Benefits & Why**
-
-| Audience | Pain-Point Solved | FastSim Value |
-| :--- | :--- | :--- |
-| **Backend Engineers** | Unsure if a 4-vCPU container can survive a marketing traffic spike. | Run *what-if* scenarios, tweak CPU cores or pool sizes, and get p95 latency and max-concurrency metrics before merging code. |
-| **DevOps / SRE** | Guesswork in capacity planning; high cost of over-provisioning. | Simulate 1 to N replicas, autoscaler thresholds, and database pool sizes to find the most cost-effective configuration that meets the SLA. |
-| **ML / LLM Product Teams** | LLM inference cost and latency are difficult to predict. | Model the LLM step with a price and latency distribution to estimate cost-per-request and the benefits of GPU batching without needing real GPUs. |
-| **Educators / Trainers** | Students struggle to visualize event-loop internals. | Visualize GIL ready-queue lag, CPU vs. I/O steps, and the effect of blocking code—perfect for live demos and labs. |
-| **Consultants / Architects** | Need a quick proof-of-concept for new client designs. | Define endpoints in YAML and demonstrate throughput and latency under projected load in minutes. |
-| **Open-Source Community** | Lacks a lightweight Python simulator for ASGI workloads. | An extensible codebase makes it easy to plug in new resources (e.g., rate-limiters, caches) or traffic models (e.g., spike, uniform ramp). |
-| **System-Design Interviewees** | Hard to quantify trade-offs in whiteboard interviews. | Prototype real-time metrics—queue lengths, concurrency, latency distributions—to demonstrate how your design scales and where bottlenecks lie. |
-
-## **4. About This Documentation**
+## **2. Installation & Quick Start**
 
-This project contains extensive documentation covering its vision, architecture, and technical implementation. The documents are designed to be read in sequence to build a comprehensive understanding of the project.
+FastSim is designed to be used as a Python library.
 
-### **How to Read This Documentation**
+```bash
+# Installation (coming soon to PyPI)
+pip install fastsim
+```
 
-For the best understanding of FastSim, we recommend reading the documentation in the following order:
+**Example Usage:**
+
+1.  Define your system topology in a `config.yml` file:
+
+    ```yaml
+    topology:
+      servers:
+        - id: "app-server-1"
+          # ... server configuration ...
+      load_balancers:
+        - id: "main-lb"
+          backends: ["app-server-1"]
+          # ... lb configuration ...
+    settings:
+      duration_s: 60
+    ```
 
-1.  **README.md (This Document)**: Start here for a high-level overview of the project's purpose, goals, target audience, and development workflow. It provides the essential context for all other documents.
-2.  **dev_worflow_guide**: This document details the github workflow for the development
-3.  **simulation_input**: This document details the technical contract for configuring a simulation. It explains the `SimulationPayload` and its components (`rqs_input`, `topology_graph`, `sim_settings`). This is essential reading for anyone who will be creating or modifying simulation configurations.
-4.  **runtime_and_resources**: A deep dive into the simulation's internal engine. It explains how the validated input is transformed into live SimPy processes (Actors, Resources, State). This is intended for advanced users or contributors who want to understand *how* the simulation works under the hood.
-5.  **requests_generator**: This document covers the mathematical and algorithmic details behind the traffic generation model. It is for those interested in the statistical foundations of the simulator.
-6.  **Simulation Metrics**: A comprehensive guide to all output metrics. It explains what each metric measures, how it's collected, and why it's important for performance analysis.
+2.  Run the simulation from a Python script:
 
-Optional **fastsim_vision**: a more detailed document about the project vision
+    ```python
+    from fastsim import run_simulation
+    from fastsim.schemas import SimulationPayload
 
-you can find the documentation at the root of the project in the folder `documentation/`
+    # Load and validate configuration using Pydantic
+    payload = SimulationPayload.from_yaml("config.yml")
 
-## **5. Development Workflow & Architecture Guide**
+    # Run the simulation
+    results = run_simulation(payload)
 
-This section outlines the standardized development workflow, repository architecture, and branching strategy for the FastSim backend.
+    # Analyze and plot results
+    results.plot_latency_distribution()
+    print(results.summary())
+    ```
 
-### **Technology Stack**
+## **3. Who Benefits & Why**
 
-  * **Backend**: FastAPI
-  * **Backend Package Manager**: Poetry
-  * **Frontend**: React + JavaScript
-  * **Database**: PostgreSQL
-  * **Caching**: Redis
-  * **Containerization**: Docker
+| Audience | Pain-Point Solved | FastSim Value |
+| :--- | :--- | :--- |
+| **Backend Engineers** | Unsure if a 4-vCPU container can survive a traffic spike. | Run *what-if* scenarios, tweak CPU cores, and get p95 latency metrics before merging code. |
+| **DevOps / SRE** | Guesswork in capacity planning; high cost of over-provisioning. | Simulate 1 to N replicas to find the most cost-effective configuration that meets the SLA. |
+| **ML / LLM Teams** | LLM inference cost and latency are difficult to predict. | Model the LLM step with a price and latency distribution to estimate cost-per-request. |
+| **Educators / Trainers** | Students struggle to visualize event-loop internals. | Visualize GIL ready-queue lag, CPU vs. I/O steps, and the effect of blocking code. |
+| **System-Design Interviewees** | Hard to quantify trade-offs in whiteboard interviews. | Prototype real-time metrics to demonstrate how your design scales and where bottlenecks lie. |
 
-### **Backend Service (`FastSim-backend`)**
+## **4. Project Structure**
 
-The repository hosts the entire FastAPI backend, which exposes the REST API, runs the discrete-event simulation, communicates with the database, and provides metrics.
+The project is a standard Python library managed with Poetry.
 
 ```
-fastsim-backend/
-├── Dockerfile
-├── docker_fs/
-│   ├── docker-compose.dev.yml
-│   └── docker-compose.prod.yml
-├── scripts/
-│   ├── init-docker-dev.sh
-│   └── quality-check.sh
-├── alembic/
-│   ├── env.py
-│   └── versions/
+fastsim/
 ├── documentation/
-│   └── backend_documentation/
-├── tests/
-│   ├── unit/
-│   └── integration/
+│   └── ...
 ├── src/
 │   └── app/
-│       ├── api/
 │       ├── config/
-│       ├── db/
 │       ├── metrics/
 │       ├── resources/
 │       ├── runtime/
-│       │   ├── rqs_state.py
-│       │   └── actors/
+│       │   ├── actors/
+│       │   └── rqs_state.py
 │       ├── samplers/
-│       ├── schemas/
-│       ├── main.py
-│       └── simulation_run.py
-├── poetry.lock
+│       └── schemas/
+├── tests/
+│   ├── unit/
+│   └── integration/
+├── .github/
+│   └── workflows/
+│       └── ci-develop.yml
 ├── pyproject.toml
+├── poetry.lock
 └── README.md
 ```
 
-### **How to Start the Backend with Docker (Development)**
+## **5. Development & Contribution**
 
-To spin up the backend and its supporting services in development mode:
-
-1.  **Install & run Docker** on your machine.
-2.  **Clone** the repository and `cd` into its root.
-3.  Execute:
-    ```bash
-    bash ./scripts/init-docker-dev.sh
-    ```
-    This will launch a **PostgreSQL** container and a **Backend** container that mounts your local `src/` folder with live-reload enabled.
-
-### **Development Architecture & Philosophy**
-
-We split responsibilities between Docker-managed services and local workflows.
-
-  * **Docker-Compose for Development**: Containers host external services (PostgreSQL) and run the FastAPI app. Your local `src/` directory is mounted into the backend container for hot-reloading. No tests, migrations, or linting run inside these containers during development.
-  * **Local Quality & Testing Workflow**: All code quality tools, migrations, and tests are executed on your host machine for faster feedback and full IDE support.
+We welcome contributions\! The development workflow is managed by Poetry and quality is enforced by Ruff and MyPy.
 
 | Task | Command | Notes |
 | :--- | :--- | :--- |
-| **Lint & format** | `poetry run ruff check src tests` | Style and best-practice validations |
-| **Type checking** | `poetry run mypy src tests` | Static type enforcement |
-| **Unit tests** | `poetry run pytest -m "not integration"` | Fast, isolated tests—no DB required |
-| **Integration tests** | `poetry run pytest -m integration` | Real-DB tests against Docker’s PostgreSQL |
-| **DB migrations** | `poetry run alembic upgrade head` | Applies migrations to your local Docker-hosted DB |
-
-**Rationale**: Running tests or Alembic migrations inside Docker images would slow down your feedback loop and limit IDE features by requiring you to mount the full source tree and install dev dependencies in each build.
-
-## **6. CI/CD with GitHub Actions**
+| **Install dependencies** | `poetry install --with dev` | Installs main and development packages. |
+| **Lint & format** | `poetry run ruff check src tests` | Style and best-practice validations. |
+| **Type checking** | `poetry run mypy src tests` | Static type enforcement. |
+| **Run all tests** | `poetry run pytest` | Executes the full test suite. |
 
-We maintain two jobs on the `develop` branch to ensure code quality and stability.
+### **CI with GitHub Actions**
 
-### **Quick (on Pull Requests)**
+We maintain two jobs on the `develop` branch to ensure code quality:
 
-  * Ruff & MyPy checks
-  * Unit tests only
-  * **No database required**
+  * **Quick (on Pull Requests):** Runs Ruff, MyPy, and unit tests for immediate feedback.
+  * **Full (on pushes to `develop`):** Runs the full suite, including integration tests and code coverage reports.
 
-### **Full (on pushes to `develop`)**
+This guarantees that every commit in `develop` is style-checked, type-safe, and fully tested.
 
-  * All checks from the "Quick" suite
-  * Starts a **PostgreSQL** service container
-  * Runs **Alembic** migrations
-  * Executes the **full test suite** (unit + integration)
-  * Builds the **Docker** image
-  * **Smoke-tests** the `/health` endpoint of the built container
+## **6. Limitations – v0.1 (First Public Release)**
 
-**Guarantee**: Every commit in `develop` is style-checked, type-safe, database-tested, and Docker-ready.
+1.  **Network Delay Model:** Only pure transport latency is simulated. Bandwidth-related effects (e.g., payload size, link speed) are not yet accounted for.
+2.  **Concurrency Model:** The simulation models a single-threaded, cooperative event-loop (like `asyncio`). Multi-process or multi-threaded parallelism is not yet supported.
+3.  **CPU Core Allocation:** Every server instance is pinned to one physical CPU core. Horizontal scaling is achieved by adding more server instances, not by using multiple cores within a single process.
 
-## **7. Limitations – v0.1 (First Public Release)**
+These constraints will be revisited in future milestones.
 
-1.  **Network Delay Model**
-      * Only pure transport latency is simulated.
-      * Bandwidth-related effects (e.g., payload size, link speed, congestion) are NOT accounted for.
-2.  **Concurrency Model**
-      * The service exposes **async-only endpoints**.
-      * Execution runs on a single `asyncio` event-loop thread.
-      * No thread-pool workers or multi-process setups are supported yet; therefore, concurrency is limited to coroutine scheduling (cooperative, single-thread).
-3.  **CPU Core Allocation**
-      * Every server instance is pinned to **one physical CPU core**.
-      * Horizontal scaling must be achieved via multiple containers/VMs, not via multi-core utilization inside a single process.
+## **7. Documentation**
 
-These constraints will be revisited in future milestones once kernel-level context-switching costs, I/O bandwidth modeling, and multi-process orchestration are integrated.
\ No newline at end of file
+For a deeper understanding of FastSim, we recommend reading the detailed documentation located in the `/documentation` folder at the root of the project. A guided reading path is suggested within to build a comprehensive understanding of the project's vision, architecture, and technical implementation.
\ No newline at end of file
diff --git a/alembic.ini b/alembic.ini
deleted file mode 100644
index 24fc464..0000000
--- a/alembic.ini
+++ /dev/null
@@ -1,144 +0,0 @@
-# A generic, single database configuration.
-
-[alembic]
-# path to migration scripts.
-# this is typically a path given in POSIX (e.g. forward slashes)
-# format, relative to the token %(here)s which refers to the location of this
-# ini file
-script_location = %(here)s/alembic
-
-# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
-# Uncomment the line below if you want the files to be prepended with date and time
-# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
-# for all available tokens
-# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
-
-# sys.path path, will be prepended to sys.path if present.
-# defaults to the current working directory.  for multiple paths, the path separator
-# is defined by "path_separator" below.
-prepend_sys_path = .
-
-
-# timezone to use when rendering the date within the migration file
-# as well as the filename.
-# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library.
-# Any required deps can installed by adding `alembic[tz]` to the pip requirements
-# string value is passed to ZoneInfo()
-# leave blank for localtime
-# timezone =
-
-# max length of characters to apply to the "slug" field
-# truncate_slug_length = 40
-
-# set to 'true' to run the environment during
-# the 'revision' command, regardless of autogenerate
-# revision_environment = false
-
-# set to 'true' to allow .pyc and .pyo files without
-# a source .py file to be detected as revisions in the
-# versions/ directory
-# sourceless = false
-
-# version location specification; This defaults
-# to <script_location>/versions.  When using multiple version
-# directories, initial revisions must be specified with --version-path.
-# The path separator used here should be the separator specified by "path_separator"
-# below.
-# version_locations = %(here)s/bar:%(here)s/bat:%(here)s/alembic/versions
-
-# path_separator; This indicates what character is used to split lists of file
-# paths, including version_locations and prepend_sys_path within configparser
-# files such as alembic.ini.
-# The default rendered in new alembic.ini files is "os", which uses os.pathsep
-# to provide os-dependent path splitting.
-#
-# Note that in order to support legacy alembic.ini files, this default does NOT
-# take place if path_separator is not present in alembic.ini.  If this
-# option is omitted entirely, fallback logic is as follows:
-#
-# 1. Parsing of the version_locations option falls back to using the legacy
-#    "version_path_separator" key, which if absent then falls back to the legacy
-#    behavior of splitting on spaces and/or commas.
-# 2. Parsing of the prepend_sys_path option falls back to the legacy
-#    behavior of splitting on spaces, commas, or colons.
-#
-# Valid values for path_separator are:
-#
-# path_separator = :
-# path_separator = ;
-# path_separator = space
-# path_separator = newline
-#
-# Use os.pathsep. Default configuration used for new projects.
-path_separator = os
-
-# set to 'true' to search source files recursively
-# in each "version_locations" directory
-# new in Alembic version 1.10
-# recursive_version_locations = false
-
-# the output encoding used when revision files
-# are written from script.py.mako
-# output_encoding = utf-8
-
-# database URL.  This is consumed by the user-maintained env.py script only.
-# other means of configuring database URLs may be customized within the env.py
-# file.
-#sqlalchemy.url = driver://user:pass@localhost/dbname
-# sqlalchemy.url is overridden at runtime in alembic/env.py 
-# via config.set_main_option(..., settings.db_url)
-# leave this here as a placeholder or for offline mode if needed
-# sqlalchemy.url = postgresql+asyncpg://user:pass@localhost:5432/dbname
-
-[post_write_hooks]
-# post_write_hooks defines scripts or Python functions that are run
-# on newly generated revision scripts.  See the documentation for further
-# detail and examples
-
-# format using "black" - use the console_scripts runner, against the "black" entrypoint
-# hooks = black
-# black.type = console_scripts
-# black.entrypoint = black
-# black.options = -l 79 REVISION_SCRIPT_FILENAME
-
-# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
-# hooks = ruff
-# ruff.type = exec
-# ruff.executable = %(here)s/.venv/bin/ruff
-# ruff.options = check --fix REVISION_SCRIPT_FILENAME
-
-# Logging configuration.  This is also consumed by the user-maintained
-# env.py script only.
-[loggers]
-keys = root,sqlalchemy,alembic
-
-[handlers]
-keys = console
-
-[formatters]
-keys = generic
-
-[logger_root]
-level = WARNING
-handlers = console
-qualname =
-
-[logger_sqlalchemy]
-level = WARNING
-handlers =
-qualname = sqlalchemy.engine
-
-[logger_alembic]
-level = INFO
-handlers =
-qualname = alembic
-
-[handler_console]
-class = StreamHandler
-args = (sys.stderr,)
-level = NOTSET
-formatter = generic
-
-[formatter_generic]
-format = %(levelname)-5.5s [%(name)s] %(message)s
-datefmt = %H:%M:%S
diff --git a/alembic/README b/alembic/README
deleted file mode 100644
index 98e4f9c..0000000
--- a/alembic/README
+++ /dev/null
@@ -1 +0,0 @@
-Generic single-database configuration.
\ No newline at end of file
diff --git a/alembic/env.py b/alembic/env.py
deleted file mode 100644
index 2e671fd..0000000
--- a/alembic/env.py
+++ /dev/null
@@ -1,83 +0,0 @@
-from logging.config import fileConfig
-
-from alembic import context
-from sqlalchemy import engine_from_config, pool
-
-from app.config.settings import settings
-from app.db.base import Base
-
-
-# this is the Alembic Config object, which provides
-# access to the values within the .ini file in use.
-config = context.config
-
-# replace sqlalchemy.url with the value of the url in env.dev
-raw_url = settings.db_url.replace("+asyncpg", "+psycopg")
-config.set_main_option("sqlalchemy.url", raw_url)
-
-
-# Interpret the config file for Python logging.
-# This line sets up loggers basically.
-if config.config_file_name is not None:
-    fileConfig(config.config_file_name)
-
-# add your model's MetaData object here
-# for 'autogenerate' support
-# from myapp import mymodel
-# target_metadata = mymodel.Base.metadata
-target_metadata = Base.metadata
-
-# other values from the config, defined by the needs of env.py,
-# can be acquired:
-# my_important_option = config.get_main_option("my_important_option")
-# ... etc.
-
-
-def run_migrations_offline() -> None:
-    """Run migrations in 'offline' mode.
-
-    This configures the context with just a URL
-    and not an Engine, though an Engine is acceptable
-    here as well.  By skipping the Engine creation
-    we don't even need a DBAPI to be available.
-
-    Calls to context.execute() here emit the given string to the
-    script output.
-
-    """
-    url = config.get_main_option("sqlalchemy.url")
-    context.configure(
-        url=url,
-        target_metadata=target_metadata,
-        literal_binds=True,
-        dialect_opts={"paramstyle": "named"},
-    )
-
-    with context.begin_transaction():
-        context.run_migrations()
-
-
-def run_migrations_online() -> None:
-    """Run migrations in 'online' mode.
-
-    In this scenario we need to create an Engine
-    and associate a connection with the context.
-
-    """
-    connectable = engine_from_config(
-        config.get_section(config.config_ini_section, {}),
-        prefix="sqlalchemy.",
-        poolclass=pool.NullPool,
-    )
-
-    with connectable.connect() as connection:
-        context.configure(connection=connection, target_metadata=target_metadata)
-
-        with context.begin_transaction():
-            context.run_migrations()
-
-
-if context.is_offline_mode():
-    run_migrations_offline()
-else:
-    run_migrations_online()
diff --git a/alembic/script.py.mako b/alembic/script.py.mako
deleted file mode 100644
index 1101630..0000000
--- a/alembic/script.py.mako
+++ /dev/null
@@ -1,28 +0,0 @@
-"""${message}
-
-Revision ID: ${up_revision}
-Revises: ${down_revision | comma,n}
-Create Date: ${create_date}
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-${imports if imports else ""}
-
-# revision identifiers, used by Alembic.
-revision: str = ${repr(up_revision)}
-down_revision: Union[str, Sequence[str], None] = ${repr(down_revision)}
-branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
-depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
-
-
-def upgrade() -> None:
-    """Upgrade schema."""
-    ${upgrades if upgrades else "pass"}
-
-
-def downgrade() -> None:
-    """Downgrade schema."""
-    ${downgrades if downgrades else "pass"}
diff --git a/docker_fs/.env.example b/docker_fs/.env.example
deleted file mode 100644
index 1ee9b47..0000000
--- a/docker_fs/.env.example
+++ /dev/null
@@ -1,8 +0,0 @@
-DB_HOST=
-DB_USER=
-DB_PASSWORD=
-DB_NAME=
-DB_URL=
-PGADMIN_DEFAULT_EMAIL=
-PGADMIN_DEFAULT_PASSWORD=
-ENVIRONMENT=
\ No newline at end of file
diff --git a/docker_fs/docker-compose.dev.yml b/docker_fs/docker-compose.dev.yml
deleted file mode 100644
index cea5fa4..0000000
--- a/docker_fs/docker-compose.dev.yml
+++ /dev/null
@@ -1,40 +0,0 @@
-services:
-  db:
-    image: postgres:17
-    ports: ["5432:5432"]
-  
-    env_file:
-      - .env.dev
-    environment:
-      POSTGRES_USER: ${DB_USER}
-      POSTGRES_PASSWORD: ${DB_PASSWORD}
-      POSTGRES_DB: ${DB_NAME}
-    
-    volumes:
-      - dev_db:/var/lib/postgresql/data
-
-  pgadmin:
-    image: dpage/pgadmin4
-    ports: ["8080:80"]
-    env_file:
-      - .env.dev
-    environment:
-      PGADMIN_DEFAULT_EMAIL: ${PGADMIN_DEFAULT_EMAIL}
-      PGADMIN_DEFAULT_PASSWORD: ${PGADMIN_DEFAULT_PASSWORD}
-    depends_on:
-      - db
-
-  backend:
-    build: ..
-    command: uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
-    volumes:
-      - ../src:/opt/app/src
-    ports:
-      - "8000:8000"
-    env_file:
-      - .env.dev
-    depends_on:
-      - db
-
-volumes:
-  dev_db:
\ No newline at end of file
diff --git a/docker_fs/docker-compose.prod.yml b/docker_fs/docker-compose.prod.yml
deleted file mode 100644
index e69de29..0000000
diff --git a/documentation/dev_workflow_guide.md b/docs/dev_workflow_guide.md
similarity index 100%
rename from documentation/dev_workflow_guide.md
rename to docs/dev_workflow_guide.md
diff --git a/docs/fastsim-docs/metrics_to_measure.md b/docs/fastsim-docs/metrics_to_measure.md
new file mode 100644
index 0000000..390f6d9
--- /dev/null
+++ b/docs/fastsim-docs/metrics_to_measure.md
@@ -0,0 +1,49 @@
+### **FastSim — Simulation Metrics**
+
+Metrics are the lifeblood of any simulation, transforming a series of abstract events into concrete, actionable insights about system performance, resource utilization, and potential bottlenecks. FastSim provides a flexible and robust metrics collection system designed to give you a multi-faceted view of your system's behavior under load.
+
+To achieve this, FastSim categorizes metrics into three distinct types:
+
+1.  **Sampled Metrics (`SampledMetricName`):** These metrics provide a **time-series view** of the system's state. They are captured at fixed, regular intervals (e.g., every 5 milliseconds). This methodology is ideal for understanding trends and measuring the continuous utilization of finite resources. Think of them as periodic snapshots of your system's health.
+
+2.  **Event-based Metrics (`EventMetricName`):** These metrics are raw data points recorded **only when a specific event occurs**, such as the completion of a request. Their collection is asynchronous and irregular. This approach is designed to capture the fundamental data needed for post-simulation analysis with maximum efficiency and flexibility.
+
+3.  **Aggregated Metrics (`AggregatedMetricName`):** These are not collected directly but are **calculated after the simulation ends**. They provide high-level statistical summaries (like mean, median, and percentiles) or rate calculations derived from the raw data collected by event-based metrics. They distill thousands of individual data points into a handful of key performance indicators (KPIs).
+
+The following sections provide a detailed breakdown of each metric within these categories.
+
+-----
+
+### **1. Sampled Metrics: A Time-Series Perspective**
+
+Sampled metrics are configured in the `SimulationSettings` payload. Enabling them allows you to plot the evolution of system resources over time, which is crucial for identifying saturation points and transient performance issues.
+
+| Metric Name (`SampledMetricName`) | Description & Rationale |
+| :--- | :--- |
+| **`READY_QUEUE_LEN`** | **What it is:** The number of tasks in the event loop's "ready" queue waiting for their turn to run on a CPU core. \<br\>\<br\> **Rationale:** This is arguably the most critical indicator of **CPU saturation**. If this queue length is consistently greater than zero, it means tasks are ready to do work but are forced to wait because the CPU is busy. A long or growing queue is a definitive sign that your application is CPU-bound. |
+| **`EVENT_LOOP_IO_SLEEP`** | **What it is:** The number of tasks currently suspended and waiting for an I/O operation to complete (e.g., a database query or a network call). \<br\>\<br\> **Rationale:** This metric helps you determine if your system is **I/O-bound**. If this queue is long, it means the CPU is potentially underutilized because it has no ready tasks to run and is instead waiting for external systems to respond. |
+| **`RAM_IN_USE`** | **What it is:** The total amount of memory (in MB) currently allocated by all active requests within a server. \<br\>\<br\> **Rationale:** Essential for **capacity planning and stability analysis**. This metric allows you to visualize your system's memory footprint under load. A steadily increasing `RAM_IN_USE` value that never returns to a baseline is the classic signature of a **memory leak**. |
+| **`EDGE_CONCURRENT_CONNECTION`** | **What it is:** The number of requests currently in transit across a network edge. \<br\>\<br\> **Rationale:** This metric helps visualize the load on your network links. A high number of concurrent connections can indicate that downstream services are slow to respond, causing requests to pile up. |
+
+-----
+
+### **2. Event-based Metrics: The Raw Data Foundation**
+
+The goal of event-based metrics is to collect the most fundamental data points with minimal overhead during the simulation. This raw data becomes the single source of truth for all post-simulation transactional analysis.
+
+| Metric Name (`EventMetricName`) | Description & Rationale |
+| :--- | :--- |
+| **`RQS_CLOCK`** | **What it is:** A collection of `(start_time, finish_time)` tuples, with one tuple recorded for every single request that is fully processed. \<br\>\<br\> **Rationale:** This is the **most efficient and flexible way to capture transactional data**. Instead of storing separate lists for latencies and completion times, we store a single, cohesive data structure. This design choice is deliberate: this raw data is all that is needed to calculate both latency and throughput after the simulation, providing maximum flexibility for analysis. |
+| **`LLM_COST`** | **What it is:** A collection of the estimated monetary cost (e.g., in USD) incurred by each individual call to an external Large Language Model (LLM) API. \<br\>\<br\> **Rationale:** In modern AI-powered applications, API calls can be a major operational expense. This metric moves beyond technical performance to measure **financial performance**, allowing for cost optimization. |
+
+-----
+
+### **3. Aggregated Metrics: Post-Simulation Insights**
+
+These metrics are calculated by an analysis module after the simulation finishes, using the raw data from the event-based metrics. This approach provides flexibility and keeps the simulation core lean.
+
+| Metric Name (`AggregatedMetricName`) | Description & Rationale |
+| :--- | :--- |
+| **`THROUGHPUT_RPS`** | **What it is:** The number of requests successfully completed per second, calculated by aggregating the `finish_time` timestamps from the `RQS_CLOCK` data over a specific time window. \<br\>\<br\> **Rationale:** This is a fundamental measure of **system performance and capacity**. Calculating it post-simulation is superior because the same raw data can be analyzed with different window sizes (e.g., per-second, per-minute) without re-running the simulation. |
+| **`LATENCY_STATS`** | **What it is:** Statistical summaries (mean, median, standard deviation, p50, p95, p99) calculated from the `RQS_CLOCK` data by taking `finish_time - start_time` for each tuple. \<br\>\<br\> **Rationale:** These statistics distill thousands of raw data points into key indicators. They tell you about the average user experience (`mean`/`p50`) and, more critically, the worst-case experience (`p95`/`p99`) needed to validate Service Level Objectives (SLOs). |
+| **`LLM_STATS`** | **What it is:** Statistical summaries (total cost, average cost per request, etc.) calculated from the raw `LLM_COST` data. \<br\>\<br\> **Rationale:** Provides high-level insights into the financial performance of AI-driven features, enabling strategic decisions on cost optimization. |
\ No newline at end of file
diff --git a/documentation/backend_documentation/requests_generator.md b/docs/fastsim-docs/requests_generator.md
similarity index 100%
rename from documentation/backend_documentation/requests_generator.md
rename to docs/fastsim-docs/requests_generator.md
diff --git a/documentation/backend_documentation/runtime_and_resources.md b/docs/fastsim-docs/runtime_and_resources.md
similarity index 69%
rename from documentation/backend_documentation/runtime_and_resources.md
rename to docs/fastsim-docs/runtime_and_resources.md
index 6c84bd2..763e0a9 100644
--- a/documentation/backend_documentation/runtime_and_resources.md
+++ b/docs/fastsim-docs/runtime_and_resources.md
@@ -176,19 +176,42 @@ An `EdgeRuntime` is a direct analog for a **physical or virtual network link**.
 
 -----
 
-### **5.3  `ServerRuntime` — The Workhorse 📦**
+### **5.3  `ServerRuntime` — The Workhorse 📦 (2025 edition)**
 
-`ServerRuntime` models an application server that owns finite CPU/RAM resources and executes a chain of steps for every incoming request.
-With the 2025 refactor it now uses a **dispatcher / handler** pattern: the dispatcher sits in an infinite loop, and each request is handled in its own SimPy subprocess. This enables many concurrent in-flight requests while keeping the code easy to reason about.
+`ServerRuntime` emulates an application server that owns **finite CPU / RAM containers** and executes an ordered chain of **Step** objects for every incoming request.
+The 2025 refactor keeps the classic **dispatcher / handler** pattern, adds **live metric counters** (ready‑queue length, I/O‑queue length, RAM‑in‑use) and implements the **lazy‑CPU lock** algorithm described earlier.
 
-| `__init__` parameter   | Meaning                                                                                                                                                                                                 |
-| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **`env`**              | The shared `simpy.Environment`. Every timeout and resource operation is scheduled here.                                                                                                                 |
-| **`server_resources`** | A `ServerContainers` mapping (`{"CPU": Container, "RAM": Container}`) created by `ResourcesRuntime`. The containers are **pre-filled** (`level == capacity`) so the server can immediately pull tokens. |
-| **`server_config`**    | The validated Pydantic `Server` model: server-wide ID, resource spec, and a list of `Endpoint` objects (each endpoint is an ordered list of `Step`s).                                                   |
-| **`out_edge`**         | The `EdgeRuntime` (or stub) that receives the `RequestState` once processing finishes.                                                                                                                  |
-| **`server_box`**       | A `simpy.Store` acting as the server’s inbox. Up-stream actors drop `RequestState`s here.                                                                                                               |
-| **`rng`**              | Instance of `numpy.random.Generator`; defaults to `default_rng()`. Used to pick a random endpoint.                                                                                                      |
+| `__init__` parameter   | Meaning                                                                                                                                                               |
+| ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **`env`**              | Shared `simpy.Environment`. Every timeout or resource operation is scheduled here.                                                                                    |
+| **`server_resources`** | A `ServerContainers` mapping `{"CPU": Container, "RAM": Container}` created by `ResourcesRuntime`. Containers start **full** so a server can immediately pull tokens. |
+| **`server_config`**    | Validated Pydantic `Server` model: ID, resource spec, list `endpoints: list[Endpoint]`.                                                                               |
+| **`out_edge`**         | `EdgeRuntime` (or stub) that receives the `RequestState` once processing finishes.                                                                                    |
+| **`server_box`**       | `simpy.Store` acting as the server’s inbox. Up‑stream actors drop `RequestState`s here.                                                                               |
+| **`rng`**              | `numpy.random.Generator`; defaults to `default_rng()`. Used to pick a random endpoint.                                                                                |
+
+---
+
+#### **Live metric fields**
+
+| Field                 | Unit     | Updated when…                                    | Used for…                                                                |
+| --------------------- | -------- | ------------------------------------------------ | ------------------------------------------------------------------------ |
+| `_el_ready_queue_len` | requests | a CPU step acquires / releases a core            | **Ready queue length** (how many coroutines wait for the GIL / a worker) |
+| `_el_io_queue_len`    | requests | an I/O step enters / leaves the socket wait list | **I/O queue length** (awaits in progress)                                |
+| `_ram_in_use`         | MB       | RAM `get` / `put`                                | Instant **RAM usage** per server                                         |
+
+Accessor properties expose them read‑only:
+
+```python
+@property
+def ready_queue_len(self) -> int: return self._el_ready_queue_len
+
+@property
+def io_queue_len(self) -> int: return self._el_io_queue_len
+
+@property
+def ram_in_use(self) -> int: return self._ram_in_use
+```
 
 ---
 
@@ -211,56 +234,111 @@ Registers the **dispatcher** coroutine in the environment and returns the create
                         │
                         ▼
             RAM get → CPU/IO steps → RAM put → out_edge.transport()
+                      ▲        │
+                      │        └── metric counters updated here
+                      └── lazy CPU lock (get once, put on first I/O)
 ```
 
 1. **Dispatcher loop**
 
-   ```python
-   while True:
-       raw_state = yield self.server_box.get()          # blocks until a request arrives
-       state = cast(RequestState, raw_state)
-       self.env.process(self._handle_request(state))    # fire-and-forget
-   ```
-
-   *Spawning a new process per request mimics worker thread concurrency.*
+```python
+while True:
+    raw_state = yield self.server_box.get()           # blocks until a request arrives
+    state = cast(RequestState, raw_state)
+    self.env.process(self._handle_request(state))     # fire‑and‑forget
+```
 
 2. **Handler coroutine (`_handle_request`)**
 
-   | Stage                           | Implementation detail                                                                                                                                                          |
-   | ------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-   | **Record arrival**              | `state.record_hop(SystemNodes.SERVER, self.server_config.id, env.now)` – leaves a breadcrumb for tracing.                                                                      |
-   | **Endpoint selection**          | Uniform random index `rng.integers(0, len(endpoints))`. (Hook point for custom routing later.)                                                                                 |
-   | **Reserve RAM (back-pressure)** | Compute `total_ram` (sum of all `StepOperation.NECESSARY_RAM`). `yield RAM.get(total_ram)`. If not enough RAM is free, the coroutine blocks, creating natural memory pressure. |
-   | **Execute steps in order**      |                                                                                                                                                                                |
-   | – CPU-bound step                | `yield CPU.get(1)` → `yield env.timeout(cpu_time)` → `yield CPU.put(1)` – exactly one core is busy for the duration.                                                           |
-   | – I/O-bound step                | `yield env.timeout(io_wait)` – no core is held, modelling non-blocking I/O.                                                                                                    |
-   | **Release RAM**                 | `yield RAM.put(total_ram)`.                                                                                                                                                    |
-   | **Forward**                     | `out_edge.transport(state)` – hands the request to the next hop without waiting for network latency.                                                                           |
+| Stage                           | Implementation detail                                                                     |
+| ------------------------------- | ----------------------------------------------------------------------------------------- |
+| **Record arrival**              | `state.record_hop(SystemNodes.SERVER, self.server_config.id, env.now)`                    |
+| **Endpoint selection**          | Uniform random index `rng.integers(0, len(endpoints))` (plug‑in point for custom routing) |
+| **Reserve RAM (back‑pressure)** | compute `total_ram` → `yield RAM.get(total_ram)` → `_ram_in_use += total_ram`             |
+| **Execute steps**               | handled in a loop with *lazy CPU lock* and metric updates (see edge‑case notes below)     |
+| **Release RAM**                 | `_ram_in_use -= total_ram` → `yield RAM.put(total_ram)`                                   |
+| **Forward**                     | `out_edge.transport(state)` — send to next hop without awaiting latency                   |
+
+---
+
+#### **CPU / I‑O loop details**
+
+* **Lazy‑CPU lock** – first CPU step acquires one core; all following contiguous CPU steps reuse it.
+* **Release on I/O** – on the first I/O step the core is released; it remains free until the next CPU step.
+* **Metric updates** – counters are modified only on the **state transition** (CPU→I/O, I/O→CPU) so there is never double‑counting.
+
+```python
+if isinstance(step.kind, EndpointStepCPU):
+    if not core_locked:
+        yield CPU.get(1)
+        core_locked = True
+        self._el_ready_queue_len += 1        # entered ready queue
+        if is_in_io_queue:
+            self._el_io_queue_len -= 1
+            is_in_io_queue = False
+    yield env.timeout(cpu_time)
+
+elif isinstance(step.kind, EndpointStepIO):
+    if core_locked:
+        yield CPU.put(1)
+        core_locked = False
+        self._el_ready_queue_len -= 1
+    if not is_in_io_queue:
+        self._el_io_queue_len += 1
+        is_in_io_queue = True
+    yield env.timeout(io_time)
+```
+
+**Handler epilogue**
+
+```python
+# at exit, remove ourselves from whichever queue we are in
+if core_locked:           # we are still in ready queue
+    self._el_ready_queue_len -= 1
+    yield CPU.put(1)
+elif is_in_io_queue:      # finished while awaiting I/O
+    self._el_io_queue_len -= 1
+```
+
+> This guarantees both queues always balance back to 0 after the last request completes.
 
 ---
 
 #### **Concurrency Guarantees**
 
-* **CPU contention** – because CPU is a token bucket (`simpy.Container`) the maximum number of concurrent CPU-bound steps equals `cpu_cores`.
-* **RAM contention** – large requests can stall entirely until enough RAM frees up, accurately modelling out-of-memory throttling.
-* **Non-blocking I/O** – while a handler waits on an I/O step it releases the core, allowing other handlers to run; this mirrors an async framework where the event loop can service other sockets.
+* **CPU contention** – the `CPU` container is a token bucket; max concurrent CPU‑bound steps = `cpu_cores`.
+* **RAM contention** – requests block at `RAM.get()` until memory is free (models cgroup / OOM throttling).
+* **Non‑blocking I/O** – while in `env.timeout(io_wait)` no core token is held, so other handlers can run; mirrors an async server where workers return to the event‑loop on each `await`.
 
 ---
 
-#### **Real-World Analogy**
+#### **Edge‑case handling (metrics)**
 
-| Runtime concept                       | Real server analogue                                                                       |
-| ------------------------------------- | ------------------------------------------------------------------------------------------ |
-| `server_box`                          | A web server’s accept queue.                                                               |
-| `CPU.get(1)`                          | Obtaining one worker thread/process in Gunicorn, uWSGI, or a Node.js “JS thread”.          |
-| `env.timeout(io_wait)` without a core | An `await` on a database or HTTP call; the worker is idle while the OS handles the socket. |
-| RAM token bucket                      | Process resident set or container memory limit; requests block when heap is exhausted.     |
+* **First‑step I/O** – counted only in I/O queue (`+1`), never touches ready queue.
+* **Consecutive I/O steps** – second I/O sees `is_in_io_queue == True`, so no extra increment (no double count).
+* **CPU → I/O → CPU** –
+   – CPU step: `core_locked=True`, `+1` ready queue
+   – I/O step: core released, `‑1` ready queue, `+1` I/O queue
+   – next CPU: core reacquired, `‑1` I/O queue, `+1` ready queue
+* **Endpoint finishes** – epilogue removes the request from whichever queue it still occupies, avoiding “ghost” entries.
+
+---
 
-Thus a **CPU-bound step** is a tight Python loop holding the GIL, while an **I/O-bound step** is `await cursor.execute(...)` that frees the event loop.
+#### **Real‑World Analogy**
+
+| Runtime concept                         | Real server analogue                                                                    |
+| --------------------------------------- | --------------------------------------------------------------------------------------- |
+| `server_box`                            | Web server accept queue (e.g., `accept()` backlog).                                     |
+| `CPU.get(1)` / `CPU.put(1)`             | Claiming / releasing a worker thread or GIL slot (Gunicorn, uWSGI, Node.js event‑loop). |
+| `env.timeout(io_wait)` (without a core) | `await redis.get()` – coroutine parked while the kernel handles the socket.             |
+| RAM token bucket                        | cgroup memory limit / container hard‑RSS; requests block when heap is exhausted.        |
+
+Thus a **CPU‑bound step** models tight Python code holding the GIL, while an **I/O‑bound step** models an `await` that yields control back to the event loop, freeing the core.
 
 ---
 
 
+
 ### **5.4. ClientRuntime: The Destination**
 
 This actor typically represents the end-user or system that initiated the request, serving as the final destination.
diff --git a/documentation/backend_documentation/simulation_input.md b/docs/fastsim-docs/simulation_input.md
similarity index 100%
rename from documentation/backend_documentation/simulation_input.md
rename to docs/fastsim-docs/simulation_input.md
diff --git a/docs/fastsim-docs/time_series_metric_architecture.md b/docs/fastsim-docs/time_series_metric_architecture.md
new file mode 100644
index 0000000..572ba71
--- /dev/null
+++ b/docs/fastsim-docs/time_series_metric_architecture.md
@@ -0,0 +1,91 @@
+## Time‑Series Metrics: Architectural Overview
+
+Collecting high‑frequency, time‑series metrics from a complex simulation requires an architecture that is **performant, maintainable, and extensible**. Our design meets those goals by keeping metric *declaration*, *state management,* and *data collection* in strictly separate layers.
+
+\### 1  Guiding Principles & Architecture
+
+1. **Minimal Hot‑Path Overhead** — every state update in the simulation core is `O(1)`.
+2. **Single Source of Truth** — one “Registry” enumerates every metric that can exist.
+3. **User‑Defined Extensibility** — advanced users can register custom metrics without touching the framework.
+4. **Predictable Memory Footprint** — data structures are pre‑allocated once, never rebuilt at each sample tick.
+
+| Layer         | Responsibility                                                   | Lifetime               |
+| ------------- | ---------------------------------------------------------------- | ---------------------- |
+| **Registry**  | Declare *which* metrics exist for each component type            | Module import (once)   |
+| **Runtime**   | Maintain the **current value** of each metric per component      | Per component instance |
+| **Collector** | Periodically read runtime values and append to time‑series lists | One per simulation run |
+
+---
+
+\### 2  Case Study — Edge Metric Collection
+
+```mermaid
+graph TD
+    subgraph Init
+        A(Registry: EDGE_METRICS) -- builds --> B{Metric Dict}
+    end
+    subgraph Loop
+        C[EdgeRuntime] -- inc/dec --> D(_concurrent_connections)
+        E[SampledMetricCollector] -->|every N ms| F{iterate}
+        F -->|read property| D
+        F -->|append| B
+    end
+    C -- owns --> B
+```
+
+#### Layer Walk‑through
+
+1. **Registry (`metrics/edge.py`)**
+
+   ```python
+   EDGE_METRICS = (SampledMetricName.EDGE_CONCURRENT_CONNECTION,)
+
+   def build_edge_metrics(enabled):
+       return {m: [] for m in EDGE_METRICS if m in enabled}
+   ```
+
+2. **Runtime (`EdgeRuntime`)**
+
+   * Updates the counter `_concurrent_connections` in `O(1)`.
+   * Holds the dict produced by `build_edge_metrics`.
+   * **New:** exposes read‑only properties so external modules never touch private fields directly.
+
+   ```python
+   class EdgeRuntime:
+       @property
+       def concurrent_connections(self) -> int:
+           return self._concurrent_connections
+
+       @property
+       def enabled_metrics(self) -> dict[SampledMetricName, list[float | int]]:
+           return self._edge_enabled_metrics
+   ```
+
+3. **Collector (`SampledMetricCollector`)**
+
+   ```python
+   while True:
+       yield env.timeout(sample_period_s)
+       for edge in self.edges:
+           key = SampledMetricName.EDGE_CONCURRENT_CONNECTION
+           # properties keep encapsulation intact
+           if key in edge.enabled_metrics:
+               edge.enabled_metrics[key].append(edge.concurrent_connections)
+   ```
+
+---
+
+\### 3  Why the `if key in …` Guard Still Matters
+
+Even with the new properties, the guard remains essential:
+
+* **Robustness** — prevents `KeyError` when a metric is disabled for a given edge.
+* **Extensibility** — a user can add `EDGE_PACKET_LOSS` (or any custom metric) to a subset of edges; the collector automatically respects that configuration.
+
+This single `O(1)` check keeps the system plug‑and‑play while preserving full encapsulation:
+
+* Runtime internals stay protected behind properties.
+* The collector never needs to know which metrics exist ahead of time.
+
+---
+
diff --git a/documentation/fastsim_vision.md b/docs/fastsim_vision.md
similarity index 100%
rename from documentation/fastsim_vision.md
rename to docs/fastsim_vision.md
diff --git a/documentation/backend_documentation/metrics_to_measure.md b/documentation/backend_documentation/metrics_to_measure.md
deleted file mode 100644
index 4de1a1a..0000000
--- a/documentation/backend_documentation/metrics_to_measure.md
+++ /dev/null
@@ -1,49 +0,0 @@
-### **FastSim — simulation's metrics**
-
-Metrics are the lifeblood of any simulation, transforming a series of abstract events into concrete, actionable insights about system performance, resource utilization, and potential bottlenecks. FastSim provides a flexible and robust metrics collection system designed to give you a multi-faceted view of your system's behavior under load.
-
-To achieve this, FastSim categorizes metrics into three distinct types based on their collection methodology:
-
-1.  **Sampled Metrics (`SampledMetricName`):** These metrics provide a **time-series view** of the system's state. They are captured at fixed, regular intervals throughout the simulation's duration (e.g., every second). This methodology is ideal for understanding trends, observing oscillations, and measuring the continuous utilization of finite resources like CPU and RAM. Think of them as periodic snapshots of your system's health.
-
-2.  **Event-based Metrics (`EventMetricName`):** These metrics are recorded **only when a specific event occurs**. Their collection is asynchronous and irregular, triggered by discrete happenings within the simulation, such as the completion of a request. This methodology is perfect for measuring the properties of individual transactions, such as end-to-end latency, where an average value is less important than understanding the full distribution of outcomes.
-
-3.  **Aggregated Metrics (`AggregatedMetricName`):** These are not collected directly during the simulation but are **calculated after the simulation ends**. They provide high-level statistical summaries (like mean, median, and percentiles) derived from the raw data collected by Event-based metrics. They distill thousands of individual data points into a handful of key performance indicators (KPIs) that are easy to interpret.
-
-The following sections provide a detailed breakdown of each metric within these categories, explaining what they measure and the rationale for their importance.
-
----
-
-### **1. Sampled Metrics: A Time-Series Perspective**
-
-Sampled metrics are configured in the `SimulationSettings` payload. Enabling them allows you to plot the evolution of system resources over time, which is crucial for identifying saturation points and transient performance issues.
-
-| Metric Name (`SampledMetricName`) | Description & Rationale |
-| :--- | :--- |
-| **`READY_QUEUE_LEN`** | **What it is:** The number of tasks in the `asyncio` event loop's "ready" queue waiting for their turn to run on the CPU. <br><br> **Rationale:** This is arguably the most critical indicator of **CPU saturation**. In a single-threaded Python process, only one coroutine can run at a time (held by the GIL). If this queue length is consistently greater than zero, it means tasks are ready to do work but are forced to wait because the CPU is busy. A long or growing queue is a definitive sign that your application is CPU-bound and that the CPU is a primary bottleneck. |
-| **`CORE_BUSY`** | **What it is:** The number of server CPU cores that are currently executing a task. <br><br> **Rationale:** This provides a direct measure of **CPU utilization**. When plotted over time, it shows how effectively you are using your provisioned processing power. If `CORE_BUSY` is consistently at its maximum value (equal to `server_resources.cpu_cores`), the system is CPU-saturated. Conversely, if it's consistently low while latency is high, the bottleneck is likely elsewhere (e.g., I/O). It perfectly complements `READY_QUEUE_LEN` to form a complete picture of CPU health. |
-| **`EVENT_LOOP_IO_SLEEP`** | **What it is:** A measure indicating if the event loop is idle, polling for I/O operations to complete. <br><br> **Rationale:** This metric helps you determine if your system is **I/O-bound**. If the event loop spends a significant amount of time in this state, it means the CPU is underutilized because it has no ready tasks to run and is instead waiting for external systems (like databases, caches, or downstream APIs) to respond. High values for this metric coupled with low CPU utilization are a clear signal to investigate and optimize the performance of your I/O operations. |
-| **`RAM_IN_USE`** | **What it is:** The total amount of memory (in MB) currently allocated by all active requests within a server. <br><br> **Rationale:** Essential for **capacity planning and stability analysis**. This metric allows you to visualize your system's memory footprint under load. You can identify which endpoints cause memory spikes and ensure your provisioned RAM is sufficient. A steadily increasing `RAM_IN_USE` value that never returns to a baseline is the classic signature of a **memory leak**, a critical bug this metric helps you detect. |
-| **`THROUGHPUT_RPS`** | **What it is:** The number of requests successfully completed per second, calculated over the last sampling window. <br><br> **Rationale:** This is a fundamental measure of **system performance and capacity**. It answers the question: "How much work is my system actually doing?" Plotting throughput against user load or other resource metrics is key to understanding your system's scaling characteristics. A drop in throughput often correlates with a spike in latency or resource saturation, helping you pinpoint the exact moment a bottleneck began to affect performance. |
-
----
-
-### **2. Event-based Metrics: A Per-Transaction Perspective**
-
-Event-based metrics are also enabled in the `SimulationSettings` payload. They generate a collection of raw data points, one for each relevant event, which is ideal for statistical analysis of transactional performance.
-
-| Metric Name (`EventMetricName`) | Description & Rationale |
-| :--- | :--- |
-| **`RQS_LATENCY`** | **What it is:** The total end-to-end duration, in seconds, for a single request to be fully processed. <br><br> **Rationale:** This is the **primary user-facing performance metric**. Users directly experience latency. While a simple average can be useful, it often hides critical problems. By collecting the latency for *every single request*, FastSim allows for the calculation of statistical distributions and, most importantly, **tail-latency percentiles (p95, p99)**. These percentiles represent the worst-case experience for your users and are crucial for evaluating Service Level Objectives (SLOs) and ensuring a consistent user experience. |
-| **`LLM_COST`** | **What it is:** The estimated monetary cost (e.g., in USD) incurred by a single call to an external Large Language Model (LLM) API during a request. <br><br> **Rationale:** In modern AI-powered applications, API calls to third-party services like LLMs can be a major operational expense. This metric moves beyond technical performance to measure **financial performance**. By tracking cost on a per-event basis, you can attribute expenses to specific endpoints or user behaviors, identify unnecessarily costly operations, and make informed decisions to optimize your application's cost-effectiveness. |
-
----
-
-### **3. Aggregated Metrics: High-Level Summaries**
-
-**Important:** Aggregated metrics are **not configured in the input payload**. They are automatically calculated by the FastSim engine at the end of a simulation run, based on the raw data collected from the enabled Event-based metrics.
-
-| Metric Name (`AggregatedMetricName`) | Description & Rationale |
-| :--- | :--- |
-| **`LATENCY_STATS`** | **What it is:** A statistical summary of the entire collection of `RQS_LATENCY` data points. This typically includes the mean, median (p50), standard deviation, and high-end percentiles (p95, p99, p99.9). <br><br> **Rationale:** This provides a comprehensive and easily digestible summary of your system's latency profile. While the raw data is essential, these summary statistics answer high-level questions quickly. The mean tells you the average experience, the median protects against outliers, and the p95/p99 values tell you the latency that 95% or 99% of your users will beat—a critical KPI for reliability and user satisfaction. |
-| **`LLM_STATS`** | **What it is:** A statistical summary of the `LLM_COST` data points. This can include total cost over the simulation, average cost per request, and cost distribution. <br><br> **Rationale:** This gives you a bird's-eye view of the financial implications of your system's design. Instead of looking at individual transaction costs, `LLM_STATS` provides the bottom line: the total operational cost during the simulation period. This is invaluable for budgeting, forecasting, and validating the financial viability of new features. |
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index b959392..e657f54 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,24 +1,5 @@
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-    {file = "websockets-15.0.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:1009ee0c7739c08a0cd59de430d6de452a55e42d6b522de7aa15e6f67db0b8e1"},
-    {file = "websockets-15.0.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76d1f20b1c7a2fa82367e04982e708723ba0e7b8d43aa643d3dcd404d74f1475"},
-    {file = "websockets-15.0.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f29d80eb9a9263b8d109135351caf568cc3f80b9928bccde535c235de55c22d9"},
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-    {file = "websockets-15.0.1-py3-none-any.whl", hash = "sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f"},
-    {file = "websockets-15.0.1.tar.gz", hash = "sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee"},
-]
-
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "71f5d3bb002c33ab3bad9d5e9efadc144b033c272b4319c2686728026dd5d849"
+content-hash = "ca95f91568cb076386715d183efe27addd2732eb4347c385a0e9f1479d2035d6"
diff --git a/pyproject.toml b/pyproject.toml
index f5fc34a..b4c46ff 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,5 +1,5 @@
 [tool.poetry]
-name = "FastSim-backend"
+name = "FastSim"
 version = "0.1.0"
 description = "Simulate fastapi event loop to manage resources"
 authors = ["Gioele Botta"]
@@ -11,25 +11,16 @@ packages = [
 
 [tool.poetry.dependencies]
 python = "^3.12"
-fastapi = "^0.115.14"
-sqlalchemy = "^2.0.41"
-psycopg = {extras = ["binary"], version = "^3.2.9"}
-uvicorn = {extras = ["standard"], version = "^0.34.3"}
-gunicorn = "^23.0.0"
 pydantic-settings = "^2.10.1"
 pydantic = {extras = ["email"], version = "^2.11.7"}
-asyncpg = "^0.30.0"
-sqlalchemy-utils = "^0.41.2"
 numpy = "^2.3.1"
 simpy = "^4.1.1"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.4.1"
 pytest-asyncio = "^1.0.0"
-httpx = "^0.28.1"
 pytest-cov = "^6.2.1"
 mypy = "^1.16.1"
-alembic = "^1.16.2"
 ruff = "^0.12.1"
 
 [build-system]
diff --git a/scripts/init-docker-dev.sh b/scripts/init-docker-dev.sh
deleted file mode 100644
index fa2c4ee..0000000
--- a/scripts/init-docker-dev.sh
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/usr/bin/env bash
-# scripts/init-docker-dev.sh 
-# Bring up local development stack using .env.dev in project root
-
-
-set -euo pipefail
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 0. Paths
-# ──────────────────────────────────────────────────────────────────────────────
-SCRIPT_PATH="$(realpath "${BASH_SOURCE[0]}")"
-SCRIPT_DIR="$(cd "$(dirname "$SCRIPT_PATH")" && pwd)"
-PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
-COMPOSE_FILE="$PROJECT_ROOT/docker_fs/docker-compose.dev.yml"
-ENV_DEV="$PROJECT_ROOT/docker_fs/.env.dev"
-ENV_DOT="$PROJECT_ROOT/docker_fs/.env"
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 0.1 Make script executable
-# ──────────────────────────────────────────────────────────────────────────────
-if [[ ! -x "$SCRIPT_PATH" ]]; then
-  chmod +x "$SCRIPT_PATH" || true
-fi
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 0.2 Ensure docker_fs/.env exists for Compose interpolation
-# ──────────────────────────────────────────────────────────────────────────────
-if [[ -f "$ENV_DEV" && ! -f "$ENV_DOT" ]]; then
-  echo ">>> Copying .env.dev → .env for Compose interpolation"
-  cp "$ENV_DEV" "$ENV_DOT"
-fi
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 1. Load env vars from .env.dev into this shell
-# ──────────────────────────────────────────────────────────────────────────────
-if [[ -f "$ENV_DEV" ]]; then
-  set -o allexport
-  source "$ENV_DEV"
-  set +o allexport
-else
-  echo "ERROR: $ENV_DEV not found. Please create it from .env.example." >&2
-  exit 1
-fi
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 2. Pull remote images (only missing ones)
-# ──────────────────────────────────────────────────────────────────────────────
-echo ">>> Pulling external service images..."
-docker compose \
-  --env-file "$ENV_DEV" \
-  -f "$COMPOSE_FILE" pull
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 3. Start Postgres + pgAdmin (detached)
-# ──────────────────────────────────────────────────────────────────────────────
-echo ">>> Starting Postgres and pgAdmin..."
-docker compose \
-  --env-file "$ENV_DEV" \
-  -f "$COMPOSE_FILE" up -d db pgadmin
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 4. Wait for Postgres to be ready, then run Alembic migrations
-# ──────────────────────────────────────────────────────────────────────────────
-PGUSER="${DB_USER:-${POSTGRES_USER:-postgres}}"
-
-echo ">>> Waiting for Postgres to be ready..."
-MAX_RETRIES=30
-RETRIES=0
-until docker exec docker_fs-db-1 pg_isready -U "$PGUSER" > /dev/null 2>&1; do
-  sleep 1
-  RETRIES=$((RETRIES+1))
-  if [ "$RETRIES" -ge "$MAX_RETRIES" ]; then
-    echo "ERROR: Postgres non si è avviato in tempo." >&2
-    exit 1
-  fi
-done
-echo ">>> Postgres is ready!"
-
-echo ">>> Applying database migrations (Alembic)…"
-cd "$PROJECT_ROOT"
-poetry run alembic upgrade head
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 5. Build (if needed) & start everything in background
-# ──────────────────────────────────────────────────────────────────────────────
-echo ">>> Building (if needed) and starting all services…"
-docker compose \
-  --env-file "$ENV_DEV" \
-  -f "$COMPOSE_FILE" up -d --build
-
-echo ">>> Development stack is up!"
-echo "    • Backend: http://localhost:8000"
-echo "    • pgAdmin: http://localhost:8080"
-echo
-echo "To tail backend logs without warnings, run:"
-echo "  docker compose \\
-  --env-file \"$ENV_DEV\" \\
-  -f \"$COMPOSE_FILE\" logs -f backend"
diff --git a/src/app/api/__init__.py b/src/app/api/__init__.py
deleted file mode 100644
index 9455eaa..0000000
--- a/src/app/api/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""API endpoints and routers."""
diff --git a/src/app/api/health_check.py b/src/app/api/health_check.py
deleted file mode 100644
index 74b4d4f..0000000
--- a/src/app/api/health_check.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""Health check API endpoints."""
-
-from fastapi import APIRouter
-
-router = APIRouter()
-
-
-@router.get("/health")
-def health_check() -> dict[str, str]:
-    """Return the health status of the application."""
-    return {"status": "ok"}
diff --git a/src/app/api/simulation.py b/src/app/api/simulation.py
deleted file mode 100644
index 2775dcc..0000000
--- a/src/app/api/simulation.py
+++ /dev/null
@@ -1,4 +0,0 @@
-""""Api to simulate the process"""
-
-
-
diff --git a/src/app/config/constants.py b/src/app/config/constants.py
index 8eeb01e..0e40950 100644
--- a/src/app/config/constants.py
+++ b/src/app/config/constants.py
@@ -13,7 +13,7 @@
 renaming existing ones.
 """
 
-from enum import IntEnum, StrEnum
+from enum import Enum, IntEnum, StrEnum
 
 # ======================================================================
 # CONSTANTS FOR THE REQUEST-GENERATOR COMPONENT
@@ -203,11 +203,22 @@ class SampledMetricName(StrEnum):
   time to create a time series
   """
 
+  # Mandatory metrics to collect
   READY_QUEUE_LEN = "ready_queue_len" #length of the event loop ready q
-  CORE_BUSY = "core_busy"
   EVENT_LOOP_IO_SLEEP = "event_loop_io_sleep"
   RAM_IN_USE = "ram_in_use"
-  THROUGHPUT_RPS = "throughput_rps"
+  EDGE_CONCURRENT_CONNECTION = "edge_concurrent_connection"
+
+
+class SamplePeriods(float, Enum):
+  """
+  defining the value of the sample periods for the metrics for which
+  we have to extract a time series
+  """
+
+  STANDARD_TIME = 0.005 # 5 MILLISECONDS
+  MINIMUM_TIME = 0.001 # 1 MILLISECOND
+  MAXIMUM_TIME = 0.1    # 10 MILLISECONDS
 
 # ======================================================================
 # CONSTANTS FOR EVENT METRICS
@@ -219,7 +230,9 @@ class EventMetricName(StrEnum):
   time series
   """
 
-  RQS_LATENCY = "rqs_latency"
+  # Mandatory
+  RQS_CLOCK = "rqs_clock"
+  # Not mandatory
   LLM_COST = "llm_cost"
 
 
@@ -231,6 +244,7 @@ class AggregatedMetricName(StrEnum):
   """aggregated metrics to calculate at the end of simulation"""
 
   LATENCY_STATS = "latency_stats"
+  THROUGHPUT_RPS = "throughput_rps"
   LLM_STATS = "llm_stats"
 
 # ======================================================================
diff --git a/src/app/config/settings.py b/src/app/config/settings.py
deleted file mode 100644
index 7fc3d9d..0000000
--- a/src/app/config/settings.py
+++ /dev/null
@@ -1,43 +0,0 @@
-"""Application settings and configuration."""
-
-from pathlib import Path
-from typing import Literal
-
-from dotenv import load_dotenv
-from pydantic import Field
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-env_path = Path(__file__).resolve().parents[3] / "docker_fs" / ".env.dev"
-if env_path.exists():
-    load_dotenv(dotenv_path=env_path)
-
-
-class Settings(BaseSettings):
-    """Application settings loaded from environment variables."""
-
-    model_config = SettingsConfigDict(env_file=None, extra="ignore")
-
-    app_name: str = "FastSim"
-
-    environment: Literal["development", "staging", "production", "test"] = Field(
-        default="development",
-        description="Runtime environment",
-        alias="ENVIRONMENT",
-    )
-
-    db_host: str = Field(default="localhost", alias="DB_HOST")
-    db_user: str = Field(default="postgres", alias="DB_USER")
-    db_password: str = Field(default="password", alias="DB_PASSWORD")
-    db_name: str = Field(default="project_db", alias="DB_NAME")
-    db_url_env: str | None = Field(default=None, alias="DB_URL")
-
-    @property
-    def db_url(self) -> str:
-        """Compute the full database URL from components if not explicitly set."""
-        if self.db_url_env:
-            return self.db_url_env
-        return f"postgresql+asyncpg://{self.db_user}:{self.db_password}@{self.db_host}/{self.db_name}"
-
-
-
-settings = Settings()
diff --git a/src/app/db/__init__.py b/src/app/db/__init__.py
deleted file mode 100644
index 8e0edc2..0000000
--- a/src/app/db/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Database modules and utilities."""
diff --git a/src/app/db/base.py b/src/app/db/base.py
deleted file mode 100644
index 2b59174..0000000
--- a/src/app/db/base.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""Database base classes and utilities."""
-
-import re
-
-from sqlalchemy.orm import DeclarativeBase, declared_attr
-
-
-class Base(DeclarativeBase):
-    """Base class for all ORM models."""
-
-    @declared_attr.directive
-    def __tablename__(cls) -> str:
-        """Generate table name from class name by converting CamelCase to snake_case."""
-        return re.sub(r"(?<!^)(?=[A-Z])", "_", cls.__name__).lower()
diff --git a/src/app/db/init_db.py b/src/app/db/init_db.py
deleted file mode 100644
index c2476b3..0000000
--- a/src/app/db/init_db.py
+++ /dev/null
@@ -1,15 +0,0 @@
-"""Database initialization and cleanup utilities."""
-
-from .base import Base
-from .session import engine
-
-
-async def init_models() -> None:
-    """Initialize database models by creating all tables."""
-    async with engine.begin() as conn:
-        await conn.run_sync(Base.metadata.create_all)
-
-
-async def close_engine() -> None:
-    """Close the database engine and dispose of connections."""
-    await engine.dispose()
diff --git a/src/app/db/session.py b/src/app/db/session.py
deleted file mode 100644
index 6e2924e..0000000
--- a/src/app/db/session.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""Database session management and connection utilities."""
-
-from collections.abc import AsyncGenerator
-
-from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
-
-from app.config.settings import settings
-
-engine = create_async_engine(
-    settings.db_url,
-    echo=False,
-    future=True,
-)
-
-AsyncSessionLocal = async_sessionmaker(
-    engine,
-    class_=AsyncSession,
-    expire_on_commit=False,
-)
-
-
-async def get_db() -> AsyncGenerator[AsyncSession, None]:
-    """
-    Dependency that provides a transactional database session.
-    It commits the transaction on successful completion or rolls back on error.
-    """
-    async with AsyncSessionLocal() as session:
-        try:
-
-            yield session
-
-            await session.commit()
-        except Exception:
-
-            await session.rollback()
-
-            raise
diff --git a/src/app/main.py b/src/app/main.py
deleted file mode 100644
index b5ef876..0000000
--- a/src/app/main.py
+++ /dev/null
@@ -1,33 +0,0 @@
-"""Main FastAPI application module."""
-
-from collections.abc import AsyncGenerator
-from contextlib import asynccontextmanager
-
-from fastapi import FastAPI
-
-from app.api.health_check import router as health_router
-from app.config.settings import settings
-from app.db.init_db import close_engine, init_models
-
-
-@asynccontextmanager
-async def lifespan(_app: FastAPI) -> AsyncGenerator[None, None]:
-    """Manage application lifespan events."""
-    # Startup
-    if settings.environment == "development":
-        await init_models()
-    yield
-    # Shutdown
-    await close_engine()
-
-
-app = FastAPI(
-    title="Project Backend",
-    version="0.1.0",
-    description="Backend service with health-check endpoint",
-    validate_response=True, #type validation of pydantic output
-    lifespan=lifespan,
-)
-
-app.include_router(health_router)
-
diff --git a/src/app/metrics/client.py b/src/app/metrics/client.py
new file mode 100644
index 0000000..2e49638
--- /dev/null
+++ b/src/app/metrics/client.py
@@ -0,0 +1,18 @@
+"""
+initialization of the structure to gather the metrics
+for the client of the system
+"""
+
+from typing import NamedTuple
+
+
+class RqsClock(NamedTuple):
+    """
+    structure to register time of generation and
+    time of elaboration for each request
+    """
+
+    start: float
+    finish: float
+
+
diff --git a/src/app/metrics/collector.py b/src/app/metrics/collector.py
new file mode 100644
index 0000000..ed85126
--- /dev/null
+++ b/src/app/metrics/collector.py
@@ -0,0 +1,70 @@
+"""class to centralized the the collection of time series regarding metrics"""
+
+from collections.abc import Generator
+
+import simpy
+
+from app.config.constants import SampledMetricName
+from app.runtime.actors.edge import EdgeRuntime
+from app.runtime.actors.server import ServerRuntime
+from app.schemas.simulation_settings_input import SimulationSettings
+
+# The idea for this class is to gather list of runtime objects that
+# are defined in the central class to build the simulation, in this
+# way we optimize the initialization of various objects reducing
+# the global overhead
+
+
+class SampledMetricCollector:
+    """class to define a centralized object to collect sampled metrics"""
+
+    def __init__(
+        self,
+        *,
+        edges: list[EdgeRuntime],
+        servers: list[ServerRuntime],
+        env:  simpy.Environment,
+        sim_settings: SimulationSettings,
+        ) -> None:
+        """Docstring to complete"""
+        self.edges = edges
+        self.servers = servers
+        self.sim_settings = sim_settings
+        self.env = env
+        self._sample_period = sim_settings.sample_period_s
+
+
+        # enum keys instance-level for mandatory sampled metrics to collect
+        self._conn_key   = SampledMetricName.EDGE_CONCURRENT_CONNECTION
+        self._ram_key    = SampledMetricName.RAM_IN_USE
+        self._io_key     = SampledMetricName.EVENT_LOOP_IO_SLEEP
+        self._ready_key  = SampledMetricName.READY_QUEUE_LEN
+
+
+        env.process(self._build_time_series())
+
+    def _build_time_series(self) -> Generator[simpy.Event, None, None]:
+        """Function to build time series for enabled metrics"""
+        while True:
+            yield self.env.timeout(self._sample_period)
+            for edge in self.edges:
+                if self._conn_key in edge.enabled_metrics:
+                    edge.enabled_metrics[self._conn_key].append(
+                        edge.concurrent_connections,
+                    )
+            for server in self.servers:
+                if all(
+                    k in server.enabled_metrics
+                    for k in (self._ram_key, self._io_key, self._ready_key)
+                ):
+                    server.enabled_metrics[self._ram_key].append(server.ram_in_use)
+                    server.enabled_metrics[self._io_key].append(server.io_queue_len)
+                    server.enabled_metrics[self._ready_key].append(server.ready_queue_len)
+
+
+
+
+
+
+
+
diff --git a/src/app/metrics/dictionary_metrics.py b/src/app/metrics/dictionary_metrics.py
deleted file mode 100644
index 189af47..0000000
--- a/src/app/metrics/dictionary_metrics.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""helpers for the simulation"""
-
-from collections.abc import Iterable
-
-from app.config.constants import EventMetricName, SampledMetricName
-
-
-def alloc_sample_metric(
-    enabled_sample_metrics: Iterable[SampledMetricName],
-    ) -> dict[str, list[float | int]]:
-    """
-    After the pydantic validation of the whole input we
-    instantiate a dictionary to collect the sampled metrics the
-    user want to measure
-    """
-    # t is the alignment parameter for example assume
-    # the snapshot for the sampled metrics are done every 10ms
-    # t = [10,20,30,40....] to each t will correspond a measured
-    # metric corresponding to that time interval
-
-    dict_sampled_metrics: dict[str, list[float | int]] = {"t": []}
-    for key in enabled_sample_metrics:
-        dict_sampled_metrics[key] = []
-    return dict_sampled_metrics
-
-
-def alloc_event_metric(
-    enabled_event_metrics: Iterable[EventMetricName],
-    ) -> dict[str, list[float | int]]:
-    """
-    After the pydantic validation of the whole input we
-    instantiate a dictionary to collect the event metrics the
-    user want to measure
-    """
-    dict_event_metrics: dict[str, list[float | int]]  = {}
-    for key in enabled_event_metrics:
-        dict_event_metrics[key] = []
-    return dict_event_metrics
diff --git a/src/app/metrics/edge.py b/src/app/metrics/edge.py
new file mode 100644
index 0000000..f714482
--- /dev/null
+++ b/src/app/metrics/edge.py
@@ -0,0 +1,29 @@
+"""initialization of the structure to gather the metrics for the edges of the system"""
+
+from collections.abc import Iterable
+
+from app.config.constants import SampledMetricName
+
+# Initialize one time outside the function all possible metrics
+# related to the edges, the idea of this structure is to
+# guarantee scalability in the long term if multiple metrics
+# will be considered
+
+EDGE_METRICS = (
+    SampledMetricName.EDGE_CONCURRENT_CONNECTION,
+)
+
+def build_edge_metrics(
+    enabled_sample_metrics: Iterable[SampledMetricName],
+    ) -> dict[SampledMetricName, list[float | int]]:
+    """
+    Function to populate a dictionary to collect values for
+    time series of sampled metrics related to the edges of
+    the system.
+    """
+    # The edge case of the empty dict is avoided since at least
+    # one metric is always measured by default.
+    return {
+        metric: [] for metric in EDGE_METRICS
+        if metric in enabled_sample_metrics
+    }
diff --git a/src/app/metrics/server.py b/src/app/metrics/server.py
new file mode 100644
index 0000000..18810ed
--- /dev/null
+++ b/src/app/metrics/server.py
@@ -0,0 +1,34 @@
+"""
+initialization of the structure to gather the sampled metrics
+for the server of the system
+"""
+
+from collections.abc import Iterable
+
+from app.config.constants import SampledMetricName
+
+# Initialize one time outside the function all possible metrics
+# related to the servers, the idea of this structure is to
+# guarantee scalability in the long term if multiple metrics
+# will be considered
+
+SERVER_METRICS = (
+    SampledMetricName.READY_QUEUE_LEN,
+    SampledMetricName.EVENT_LOOP_IO_SLEEP,
+    SampledMetricName.RAM_IN_USE,
+)
+
+def build_server_metrics(
+    enabled_sample_metrics: Iterable[SampledMetricName],
+    ) -> dict[SampledMetricName, list[float | int]]:
+    """
+    Function to populate a dictionary to collect values for
+    time series of sampled metrics related to the server of
+    the system.
+    """
+    # The edge case of the empty dict is avoided since at least
+    # one metric is always measured by default.
+    return {
+        metric: [] for metric in SERVER_METRICS
+        if metric in enabled_sample_metrics
+    }
diff --git a/src/app/runtime/actors/client.py b/src/app/runtime/actors/client.py
index 207bd71..f5d63ac 100644
--- a/src/app/runtime/actors/client.py
+++ b/src/app/runtime/actors/client.py
@@ -6,6 +6,7 @@
 import simpy
 
 from app.config.constants import SystemNodes
+from app.metrics.client import RqsClock
 from app.runtime.actors.edge import EdgeRuntime
 from app.schemas.system_topology.full_system_topology import Client
 
@@ -31,6 +32,11 @@ def __init__(
         self.client_config = client_config
         self.client_box = client_box
         self.completed_box = completed_box
+        # This list will be enough to calculate at the end
+        # of the simulation both the throughput and the
+        # latency distribution
+
+        self._rqs_clock: list[RqsClock] = []
 
 
     def _forwarder(self) -> Generator[simpy.Event, None, None]:
@@ -44,12 +50,19 @@ def _forwarder(self) -> Generator[simpy.Event, None, None]:
                     self.env.now,
                 )
 
-            # by checking the previous node (-2 the previous component is an edge)
-            # we are able to understand if the request should be elaborated
-            # when the type is Generator, or the request is completed, in this case
-            # the client is the target and the previous node type is not a rqs generator
-            if state.history[-2].component_type != SystemNodes.GENERATOR:
+            # if the length of the list is bigger than two
+            # it means that the state is coming back to the
+            # client after being elaborated, since if the value
+            # would be equal to two would mean that the state
+            # went through the mandatory path to be generated
+            # rqs generator and client registration
+            if len(state.history) > 2:
                 state.finish_time = self.env.now
+                clock_data = RqsClock(
+                    start=state.initial_time,
+                    finish=state.finish_time,
+                )
+                self._rqs_clock.append(clock_data)
                 yield self.completed_box.put(state)
             else:
                 self.out_edge.transport(state)
@@ -57,3 +70,11 @@ def _forwarder(self) -> Generator[simpy.Event, None, None]:
     def start(self) -> simpy.Process:
         """Initialization of the process"""
         return self.env.process(self._forwarder())
+
+    @property
+    def rqs_clock(self) -> list[RqsClock]:
+        """
+        Expose the value of the private list of the starting
+        and arrival time for each rqs just for reading purpose
+        """
+        return self._rqs_clock
diff --git a/src/app/runtime/actors/edge.py b/src/app/runtime/actors/edge.py
index afef720..4e2f549 100644
--- a/src/app/runtime/actors/edge.py
+++ b/src/app/runtime/actors/edge.py
@@ -12,9 +12,11 @@
 import numpy as np
 import simpy
 
-from app.config.constants import SystemEdges
+from app.config.constants import SampledMetricName, SystemEdges
+from app.metrics.edge import build_edge_metrics
 from app.runtime.rqs_state import RequestState
 from app.samplers.common_helpers import general_sampler
+from app.schemas.simulation_settings_input import SimulationSettings
 from app.schemas.system_topology.full_system_topology import Edge
 
 if TYPE_CHECKING:
@@ -32,12 +34,26 @@ def __init__(
         edge_config: Edge,
         rng: np.random.Generator | None = None,
         target_box: simpy.Store,
+        settings: SimulationSettings,
         ) -> None:
         """Definition of the instance attributes"""
         self.env = env
         self.edge_config = edge_config
         self.target_box = target_box
         self.rng = rng or np.random.default_rng()
+        self.setting = settings
+        self._edge_enabled_metrics = build_edge_metrics(
+            settings.enabled_sample_metrics,
+        )
+        self._concurrent_connections: int = 0
+
+        # We keep a reference to `settings` because this class needs to observe but not
+        # persist the edge-related metrics the user has enabled.
+        # The actual persistence (appending snapshots to the time series lists)
+        # is handled centrally in metrics/collector.py,which runs every Xmilliseconds.
+        # Here we only expose the current metric values, guarded by a few if checks to
+        # verify that each optional metric is active. For deafult metric settings
+        # is not needed but as we will scale as explained above we will need it
 
     def _deliver(self, state: RequestState) -> Generator[simpy.Event, None, None]:
         """Function to deliver the state to the next node"""
@@ -54,13 +70,17 @@ def _deliver(self, state: RequestState) -> Generator[simpy.Event, None, None]:
             )
             return
 
+        self._concurrent_connections +=1
+
         transit_time = general_sampler(random_variable, self.rng)
         yield self.env.timeout(transit_time)
+
         state.record_hop(
             SystemEdges.NETWORK_CONNECTION,
             self.edge_config.id,
             self.env.now,
             )
+        self._concurrent_connections -=1
         yield self.target_box.put(state)
 
 
@@ -71,7 +91,15 @@ def transport(self, state: RequestState) -> simpy.Process:
         """
         return self.env.process(self._deliver(state))
 
+    @property
+    def enabled_metrics(self) -> dict[SampledMetricName, list[float | int]]:
+        """Read-only access to the metric store."""
+        return self._edge_enabled_metrics
 
+    @property
+    def concurrent_connections(self) -> int:
+        """Current number of open connections on this edge."""
+        return self._concurrent_connections
 
 
 
diff --git a/src/app/runtime/actors/server.py b/src/app/runtime/actors/server.py
index 0a0f772..94d6b24 100644
--- a/src/app/runtime/actors/server.py
+++ b/src/app/runtime/actors/server.py
@@ -13,13 +13,16 @@
     EndpointStepCPU,
     EndpointStepIO,
     EndpointStepRAM,
+    SampledMetricName,
     ServerResourceName,
     StepOperation,
     SystemNodes,
 )
+from app.metrics.server import build_server_metrics
 from app.resources.server_containers import ServerContainers
 from app.runtime.actors.edge import EdgeRuntime
 from app.runtime.rqs_state import RequestState
+from app.schemas.simulation_settings_input import SimulationSettings
 from app.schemas.system_topology.full_system_topology import Server
 
 
@@ -33,28 +36,34 @@ def __init__(  # noqa: PLR0913
         server_config: Server,
         out_edge: EdgeRuntime,
         server_box: simpy.Store,
+        settings: SimulationSettings,
         rng: np.random.Generator | None = None,
     ) -> None:
-        """Server attributes
-
-        Args:
-            env (simpy.Environment): _description_
-            server_resources (ServerContainers): _description_
-            server_config (Server): _description_
-            out_edge (EdgeRuntime): _description_
-            server_box (simpy.Store): _description_
-            rng (np.random.Generator | None, optional): _description_. Defaults to None.
-
-        """
+        """Docstring to complete"""
         self.env = env
         self.server_resources = server_resources
         self.server_config = server_config
         self.out_edge = out_edge
         self.server_box = server_box
         self.rng = rng or np.random.default_rng()
+        # length of the active queue of the event loop
+        self._el_ready_queue_len: int = 0
+        # total ram used in the server
+        self._ram_in_use: int | float = 0
+        # length of the queue of the I/O task of the vent loop
+        self._el_io_queue_len: int = 0
 
+        # Right now is not necessary but as we will introduce
+        # non mandatory metrics we will need this structure to
+        # check if we have to measure a given metric
+        # right now it is not necessary because we are dealing
+        # only with mandatory metrics
+        self._server_enabled_metrics = build_server_metrics(
+            settings.enabled_sample_metrics,
+        )
 
-    def _handle_request(
+    # right now we disable the warnings but a refactor will be done soon
+    def _handle_request( # noqa: PLR0915, PLR0912, C901
         self,
         state: RequestState,
         ) -> Generator[simpy.Event, None, None]:
@@ -79,45 +88,175 @@ def _handle_request(
         selected_endpoint_idx = self.rng.integers(low=0, high=endpoints_number)
         selected_endpoint = endpoints_list[selected_endpoint_idx]
 
-        # RAM management:
-        # first calculate the ram needed
-        # Ask if it is available
-        # Release everything when the operation completed
 
+        # Extract the total ram to execute the endpoint
         total_ram = sum(
             step.step_operation[StepOperation.NECESSARY_RAM]
             for step in selected_endpoint.steps
             if isinstance(step.kind, EndpointStepRAM)
         )
 
+        # ------------------------------------------------------------------
+        # CPU & RAM SCHEDULING
+        #
+        #  RAM FIRST, CPU LATER
+        #   - The request reserves its full working set (total_ram) before
+        #     acquiring any CPU core. If memory isn't available, it stays
+        #     queued and leaves cores free for other requests.
+        #
+        #  LAZY-CPU LOCK
+        #   - A core token is acquired only at the FIRST CPU step
+        #     (`if not core_locked`) and held for all consecutive CPU steps.
+        #   - As soon as an I/O step is encountered, the core is released
+        #     (`CPU.put(1)`) and remains free until the next CPU step,
+        #     which will re-acquire it.
+        #
+        #  WHY THIS IS REALISTIC
+        #    Prevents “core-hogging” during long I/O awaits.
+        #    Avoids redundant get/put calls for consecutive CPU steps
+        #      (one token for the entire sequence).
+        #    Mirrors a real Python async server: the GIL/worker thread is
+        #      held only during CPU-bound code and released on each await.
+        #
+        #  END OF HANDLER
+        #   - If we still hold the core at the end (`core_locked == True`),
+        #     we put it back, then release the reserved RAM.
+        # ------------------------------------------------------------------
 
+        # Ask the necessary ram to the server
         if total_ram:
             yield self.server_resources[ServerResourceName.RAM.value].get(total_ram)
+            self._ram_in_use += total_ram
+
+        # Initial conditions of the server a rqs a priori is not in any queue
+        # and it does not occupy a core until it started to be elaborated
+        core_locked = False
+        is_in_io_queue = False
+        is_in_ready_queue = False
 
 
         # --- Step Execution: Process CPU and IO operations ---
+        #  EDGE CASE
+        #  First-step I/O
+        #     A request could (in theory) start with an I/O step. In that case
+        #     it doesn't hold any core; we enter the
+        #     `not core_locked and not is_in_io_queue` branch and add +1
+        #     to the I/O queue without touching the ready queue.
+        #
+        #  Consecutive I/O steps
+        #     The second I/O sees `is_in_io_queue == True`, so it does NOT
+        #     increment again—no double counting.
+        #
+        #  Transition CPU → I/O → CPU
+        #     - CPU step: `core_locked` becomes True, +1 ready queue
+        #     - I/O step: core is put back, -1 ready queue, +1 I/O queue
+        #     - Next CPU step: core is acquired, -1 I/O queue, +1 ready queue
+        #
+        #  Endpoint completion
+        #     If `core_locked == True`  we were in the ready queue (-1)
+        #     Otherwise  we were in the I/O queue (-1)
+        #     In both cases we clear the local flags so no “ghost” entries
+        #     remain in the global counters.
+        # ------------------------------------------------------------------
+
+
         for step in selected_endpoint.steps:
 
             if isinstance(step.kind, EndpointStepCPU):
-                cpu_time = step.step_operation[StepOperation.CPU_TIME]
+                # with the boolean we avoid redundant operation of asking
+                # the core multiple time on a given step
+                # for example if we have two consecutive cpu bound step
+                # in this configuration we are asking the cpu just in the
+                # first one
 
-                # Acquire one core
-                yield self.server_resources[ServerResourceName.CPU.value].get(1)
-                # Hold the core busy
+                if not core_locked:
+                    core_locked = True
+
+                    if is_in_io_queue:
+                        is_in_io_queue = False
+                        self._el_io_queue_len -= 1
+
+                    if not is_in_ready_queue:
+                        is_in_ready_queue = True
+                        self._el_ready_queue_len += 1
+
+
+                    yield self.server_resources[ServerResourceName.CPU.value].get(1)
+
+                cpu_time = step.step_operation[StepOperation.CPU_TIME]
+                # Execute the step giving back the control to the simpy env
                 yield self.env.timeout(cpu_time)
-                # Release the core
-                yield self.server_resources[ServerResourceName.CPU.value].put(1)
+
 
             elif isinstance(step.kind, EndpointStepIO):
                 io_time = step.step_operation[StepOperation.IO_WAITING_TIME]
+                # Same here with the boolean if we have multiple I/O steps
+                # we release the core just the first time if the previous step
+                # was a cpu bound step
+
+                if not core_locked and not is_in_io_queue:
+                    is_in_io_queue = True
+                    self._el_io_queue_len += 1
+
+
+                if core_locked:
+                    # if the core is locked in the function it means that for sure
+                    # we had a cpu bound step so the if statement will be always
+                    # satisfy and we have to remove one element from the ready queue
+
+                    if is_in_ready_queue:
+                        is_in_ready_queue = False
+                        self._el_ready_queue_len -= 1
+
+                    if not is_in_io_queue:
+                        is_in_io_queue = True
+                        self._el_io_queue_len += 1
+
+                    yield self.server_resources[ServerResourceName.CPU.value].put(1)
+                    core_locked = False
                 yield self.env.timeout(io_time)  # Wait without holding a CPU core
 
-        # release the ram
+
+        if core_locked:
+            is_in_ready_queue = False
+            self._el_ready_queue_len -= 1
+            yield self.server_resources[ServerResourceName.CPU.value].put(1)
+        else:
+            is_in_io_queue = False
+            self._el_io_queue_len -= 1
+
         if total_ram:
+
+            self._ram_in_use -= total_ram
             yield self.server_resources[ServerResourceName.RAM.value].put(total_ram)
 
         self.out_edge.transport(state)
 
+
+    # we need three accessor because we need to read these private attribute
+    # in the sampled metric collector
+    @property
+    def ready_queue_len(self) -> int:
+        """Current length of the event-loop ready queue for this server."""
+        return self._el_ready_queue_len
+
+    @property
+    def io_queue_len(self) -> int:
+        """Current length of the event-loop I/O queue for this server."""
+        return self._el_io_queue_len
+
+    @property
+    def ram_in_use(self) -> int | float:
+        """Total RAM (MB) currently reserved by active requests."""
+        return self._ram_in_use
+
+    @property
+    def enabled_metrics(self) -> dict[SampledMetricName, list[float | int]]:
+        """Read-only access to the metric store."""
+        return self._server_enabled_metrics
+
+
+
     def _dispatcher(self) -> Generator[simpy.Event, None, None]:
         """
         The main dispatcher loop. It pulls requests from the inbox and
diff --git a/src/app/schemas/simulation_settings_input.py b/src/app/schemas/simulation_settings_input.py
index 5d0ac6a..80828b5 100644
--- a/src/app/schemas/simulation_settings_input.py
+++ b/src/app/schemas/simulation_settings_input.py
@@ -2,7 +2,12 @@
 
 from pydantic import BaseModel, Field
 
-from app.config.constants import EventMetricName, SampledMetricName, TimeDefaults
+from app.config.constants import (
+    EventMetricName,
+    SampledMetricName,
+    SamplePeriods,
+    TimeDefaults,
+)
 
 
 class SimulationSettings(BaseModel):
@@ -14,18 +19,27 @@ class SimulationSettings(BaseModel):
         description="Simulation horizon in seconds.",
     )
 
+    # These represent the mandatory metrics to collect
     enabled_sample_metrics: set[SampledMetricName] = Field(
         default_factory=lambda: {
             SampledMetricName.READY_QUEUE_LEN,
-            SampledMetricName.CORE_BUSY,
+            SampledMetricName.EVENT_LOOP_IO_SLEEP,
             SampledMetricName.RAM_IN_USE,
+            SampledMetricName.EDGE_CONCURRENT_CONNECTION,
         },
         description="Which time-series KPIs to collect by default.",
     )
     enabled_event_metrics: set[EventMetricName] = Field(
         default_factory=lambda: {
-            EventMetricName.RQS_LATENCY,
+            EventMetricName.RQS_CLOCK,
         },
         description="Which per-event KPIs to collect by default.",
     )
 
+    sample_period_s: float = Field(
+        default = SamplePeriods.STANDARD_TIME,
+        ge = SamplePeriods.MINIMUM_TIME,
+        le = SamplePeriods.MAXIMUM_TIME,
+        description="constant interval of time to build time series for metrics",
+    )
+
diff --git a/src/app/simulation_run.py b/src/app/simulation_run.py
deleted file mode 100644
index 1176751..0000000
--- a/src/app/simulation_run.py
+++ /dev/null
@@ -1,60 +0,0 @@
-"""simulation of the server"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-import simpy
-
-from app.core.helpers.requests_generator import requests_generator
-from app.schemas.simulation_output import SimulationOutput
-
-if TYPE_CHECKING:
-    from collections.abc import Generator
-
-    import numpy as np
-
-    from app.schemas.full_simulation_input import SimulationPayload
-
-
-
-
-### TO MODIFY EVERYTHING WORK IN PROGRESS
-
-def run_simulation(
-    input_data: SimulationPayload,
-    *,
-    rng: np.random.Generator,
-) -> SimulationOutput:
-    """Simulation executor in Simpy"""
-    sim_settings = input_data.sim_settings
-
-    requests_generator_input = input_data.rqs_input
-
-    gaps: Generator[float, None, None] = requests_generator(
-        requests_generator_input,
-        sim_settings,
-        rng=rng)
-    env = simpy.Environment()
-
-
-    total_request_per_time_period = {
-        "simulation_time": sim_settings.total_simulation_time,
-        "total_requests": 0,
-        }
-
-    def arrival_process(
-        env: simpy.Environment,
-    ) -> Generator[simpy.events.Event, None, None]:
-        for gap in gaps:
-            yield env.timeout(gap)
-            total_request_per_time_period["total_requests"] += 1
-
-    env.process(arrival_process(env))
-    env.run(until=sim_settings.total_simulation_time)
-
-    return SimulationOutput(
-        total_requests=total_request_per_time_period,
-        metric_2=str(requests_generator_input.avg_request_per_minute_per_user.mean),
-        metric_n=str(requests_generator_input.avg_active_users.mean),
-    )
diff --git a/tests/conftest.py b/tests/conftest.py
index 7d0f587..09a80d4 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,33 +1,16 @@
 """Pytest configuration file for setting up test fixtures and plugins."""
 
-import os
-from collections.abc import AsyncGenerator, Generator
-from pathlib import Path
-from typing import cast
 
 import pytest
-from alembic import command
-from alembic.config import Config
-from dotenv import load_dotenv
-from fastapi.testclient import TestClient
 from numpy.random import Generator as NpGenerator
 from numpy.random import default_rng
-from sqlalchemy.ext.asyncio import (
-    AsyncEngine,
-    AsyncSession,
-    async_sessionmaker,
-    create_async_engine,
-)
-from sqlalchemy_utils import create_database, database_exists, drop_database
 
 from app.config.constants import (
     EventMetricName,
     SampledMetricName,
+    SamplePeriods,
     TimeDefaults,
 )
-from app.config.settings import settings
-from app.db.session import get_db
-from app.main import app
 from app.schemas.full_simulation_input import SimulationPayload
 from app.schemas.random_variables_config import RVConfig
 from app.schemas.rqs_generator_input import RqsGeneratorInput
@@ -38,106 +21,6 @@
     TopologyNodes,
 )
 
-# Load test environment variables from .env.test
-ENV_PATH = Path(__file__).resolve().parents[1] / "docker_fs" / ".env.test"
-load_dotenv(dotenv_path=ENV_PATH, override=True)
-
-
-class DummySession:
-    """A no-op async session substitute for unit tests."""
-
-    def add(self, instance: object) -> None:
-        """Perform a no-op add."""
-
-    async def commit(self) -> None:
-        """Perform a no-op commit."""
-
-    async def refresh(self, instance: object) -> None:
-        """Perform a no-op refresh."""
-
-    async def flush(self) -> None:
-        """Perform a no-op flush, required for unit tests."""
-
-
-async def override_get_db() -> AsyncGenerator[AsyncSession, None]:
-    """Yield a dummy async session for DB dependency override in unit tests."""
-    # Cast so mypy sees an AsyncSession
-    yield cast("AsyncSession", DummySession())
-
-
-# Override the get_db dependency for all tests that use the app directly
-app.dependency_overrides[get_db] = override_get_db
-
-
-@pytest.fixture(scope="module")
-def client() -> TestClient:
-    """Return a TestClient with the database dependency overridden."""
-    return TestClient(app)
-
-
-@pytest.fixture(scope="session", autouse=True)
-def setup_test_database(
-    request: pytest.FixtureRequest,
-) -> Generator[None, None, None]:
-    """Drop and recreate the test database, then apply migrations."""
-    markexpr = request.config.option.markexpr or ""
-    is_integration_test = (
-        "integration" in markexpr and "not integration" not in markexpr
-    )
-
-    if not is_integration_test:
-        yield
-        return
-
-    # --- SETUP: This code runs before the test session starts ---
-    sync_db_url = settings.db_url.replace("+asyncpg", "")
-
-    if database_exists(sync_db_url):
-        drop_database(sync_db_url)
-    create_database(sync_db_url)
-
-    os.environ["ENVIRONMENT"] = "test"
-    alembic_cfg = Config("alembic.ini")
-    command.upgrade(alembic_cfg, "head")
-
-    try:
-        # The test session runs at this point
-        yield
-    finally:
-        # --- TEARDOWN: This code runs after the test session ends ---
-        if database_exists(sync_db_url):
-            drop_database(sync_db_url)
-
-
-@pytest.fixture(scope="session")
-def async_engine() -> AsyncEngine:
-    """Create and return an async SQLAlchemy engine for the test session."""
-    return create_async_engine(settings.db_url, future=True, echo=False)
-
-
-@pytest.fixture
-async def db_session(async_engine: AsyncEngine) -> AsyncGenerator[AsyncSession, None]:
-    """Yield an async DB session wrapped in a transaction that is always rolled back."""
-    # This fixture ensures that each test is isolated from others
-    # within the same session.
-    connection = await async_engine.connect()
-    transaction = await connection.begin()
-
-    session_factory = async_sessionmaker(
-        bind=connection,
-        expire_on_commit=False,
-        class_=AsyncSession,
-    )
-    session = session_factory()
-
-    try:
-        yield session
-    finally:
-        # Roll back the transaction to discard any changes made during the test
-        await transaction.rollback()
-        # Close the connection
-        await connection.close()
-
 # ============================================================================
 # STANDARD CONFIGURATION FOR INPUT VARIABLES
 # ============================================================================
@@ -171,7 +54,7 @@ def enabled_sample_metrics() -> set[SampledMetricName]:
 def enabled_event_metrics() -> set[EventMetricName]:
     """Default per-event KPIs collected in most tests."""
     return {
-        EventMetricName.RQS_LATENCY,
+        EventMetricName.RQS_CLOCK,
     }
 
 
@@ -195,6 +78,7 @@ def sim_settings(
         total_simulation_time=TimeDefaults.MIN_SIMULATION_TIME,
         enabled_sample_metrics=enabled_sample_metrics,
         enabled_event_metrics=enabled_event_metrics,
+        sample_period_s=SamplePeriods.STANDARD_TIME,
     )
 
 
diff --git a/tests/integration/db_initialization/__init__.py b/tests/integration/db_initialization/__init__.py
deleted file mode 100644
index 8c00603..0000000
--- a/tests/integration/db_initialization/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Database initialization integration tests."""
diff --git a/tests/integration/db_initialization/test_db_connection.py b/tests/integration/db_initialization/test_db_connection.py
deleted file mode 100644
index 8f725ee..0000000
--- a/tests/integration/db_initialization/test_db_connection.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import pytest
-from sqlalchemy import text
-
-from app.db.session import engine
-
-
-@pytest.mark.integration
-async def test_db_connection() -> None:
-    """Verify that the SQLAlchemy engine can connect to the database.
-
-    This test ensures that the database connection is properly configured
-    and can execute a simple query.
-    """
-    async with engine.connect() as conn:
-        result = await conn.execute(text("SELECT 1"))
-        assert result.scalar_one() == 1
diff --git a/tests/integration/db_initialization/test_init_models.py b/tests/integration/db_initialization/test_init_models.py
deleted file mode 100644
index 0ae8e69..0000000
--- a/tests/integration/db_initialization/test_init_models.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import pytest
-from sqlalchemy import text
-from sqlalchemy.exc import SQLAlchemyError
-from sqlalchemy.ext.asyncio import create_async_engine
-
-from app.config.settings import settings
-
-pytestmark = [pytest.mark.integration, pytest.mark.asyncio]
-
-async def test_users_table_exists_after_migrations() -> None:
-    engine = create_async_engine(settings.db_url, echo=False)
-    try:
-        async with engine.connect() as conn:
-           await conn.execute(text("SELECT 1"))
-    except SQLAlchemyError:
-        pytest.fail("Database connection or Alembic setup failed.")
-
diff --git a/tests/unit/db/test_settings.py b/tests/unit/db/test_settings.py
deleted file mode 100644
index 9a277bc..0000000
--- a/tests/unit/db/test_settings.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from app.config.settings import Settings
-
-
-def test_settings_defaults() -> None:
-    """Ensure that default settings are applied correctly.
-
-    This test verifies that the Settings class properly handles explicit configuration
-    values and applies the correct defaults for unspecified fields.
-    """
-    s = Settings(
-        db_host="localhost",
-        db_user="x",
-        db_password="y",
-        db_name="z",
-        db_url="postgresql+asyncpg://x:y@localhost/z",
-    )
-    assert s.environment == "test"
-    assert "postgresql" in s.db_url
diff --git a/tests/unit/runtime/engine/test_edge.py b/tests/unit/runtime/engine/test_edge.py
index 10cb758..37ad296 100644
--- a/tests/unit/runtime/engine/test_edge.py
+++ b/tests/unit/runtime/engine/test_edge.py
@@ -1,20 +1,28 @@
-"""Unit-tests for :class:`EdgeRuntime` (delivery / drop paths)."""
+"""
+Unit tests for :class:`EdgeRuntime`:
+* delivery vs. drop paths
+* connection-counter bookkeeping
+* public properties (`enabled_metrics`, `concurrent_connections`)
+"""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, cast
 
 import simpy
 
-from app.config.constants import SystemEdges, SystemNodes
+from app.config.constants import SampledMetricName, SystemEdges, SystemNodes
 from app.runtime.actors.edge import EdgeRuntime
 from app.runtime.rqs_state import RequestState
 from app.schemas.random_variables_config import RVConfig
 from app.schemas.system_topology.full_system_topology import Edge
 
 if TYPE_CHECKING:
-
     import numpy as np
 
+    from app.schemas.simulation_settings_input import SimulationSettings
+
+
 # --------------------------------------------------------------------------- #
 # Dummy RNG                                                                   #
 # --------------------------------------------------------------------------- #
@@ -24,26 +32,38 @@ class DummyRNG:
     """Return preset values for ``uniform`` and ``normal``."""
 
     def __init__(self, *, uniform_value: float, normal_value: float = 0.0) -> None:
-        """Attribute init"""
+        """To complete"""
         self.uniform_value = uniform_value
         self.normal_value = normal_value
         self.uniform_called = False
         self.normal_called = False
 
-
-    def uniform(self) -> float:
-        """Intercept ``rng.uniform`` calls."""
+    def uniform(self) -> float:  # called by EdgeRuntime
+        """To complete"""
         self.uniform_called = True
         return self.uniform_value
 
-    def normal(self, _mean: float, _sigma: float) -> float:
-        """Intercept ``rng.normal`` calls."""
+    def normal(self, _mean: float, _sigma: float) -> float:  # called by sampler
+        """To complete"""
         self.normal_called = True
         return self.normal_value
 
 
 # --------------------------------------------------------------------------- #
-# Helper to build an EdgeRuntime                                              #
+# Minimal stub for SimulationSettings                                         #
+# --------------------------------------------------------------------------- #
+
+
+class _SettingsStub:
+    """Only the attributes required by EdgeRuntime/build_edge_metrics."""
+
+    def __init__(self, enabled_sample_metrics: set[SampledMetricName]) -> None:
+        self.enabled_sample_metrics = enabled_sample_metrics
+        self.sample_period_s = 0.001  # not used in these unit tests
+
+
+# --------------------------------------------------------------------------- #
+# Helper factory                                                              #
 # --------------------------------------------------------------------------- #
 
 
@@ -54,10 +74,10 @@ def _make_edge(
     normal_value: float = 0.0,
     dropout_rate: float = 0.0,
 ) -> tuple[EdgeRuntime, DummyRNG, simpy.Store]:
-    """Attributes init"""
+    """Create a fully wired :class:`EdgeRuntime` + associated objects."""
     rng = DummyRNG(uniform_value=uniform_value, normal_value=normal_value)
-
     store: simpy.Store = simpy.Store(env)
+
     edge_cfg = Edge(
         id="edge-1",
         source="src",
@@ -66,11 +86,16 @@ def _make_edge(
         dropout_rate=dropout_rate,
     )
 
+    settings_stub = _SettingsStub(
+        enabled_sample_metrics={SampledMetricName.EDGE_CONCURRENT_CONNECTION},
+    )
+
     edge_rt = EdgeRuntime(
         env=env,
         edge_config=edge_cfg,
         rng=cast("np.random.Generator", rng),
         target_box=store,
+        settings=cast("SimulationSettings", settings_stub),
     )
     return edge_rt, rng, store
 
@@ -80,8 +105,8 @@ def _make_edge(
 # --------------------------------------------------------------------------- #
 
 
-def test_edge_delivers_message_when_not_dropped() -> None:
-    """A message traverses the edge and calls the latency sampler once."""
+def test_edge_delivers_message() -> None:
+    """A request traverses the edge when `uniform >= dropout_rate`."""
     env = simpy.Environment()
     edge_rt, rng, store = _make_edge(
         env,
@@ -93,24 +118,32 @@ def test_edge_delivers_message_when_not_dropped() -> None:
     state = RequestState(id=1, initial_time=0.0)
     state.record_hop(SystemNodes.GENERATOR, "gen-1", env.now)
 
+    assert edge_rt.concurrent_connections == 0
+
     edge_rt.transport(state)
     env.run()
 
+    # message delivered
     assert len(store.items) == 1
     delivered: RequestState = store.items[0]
     last = delivered.history[-1]
     assert last.component_type is SystemEdges.NETWORK_CONNECTION
     assert last.component_id == "edge-1"
+
+    # RNG calls
     assert rng.uniform_called is True
     assert rng.normal_called is True
 
+    # counter restored
+    assert edge_rt.concurrent_connections == 0
+
 
-def test_edge_drops_message_when_uniform_below_threshold() -> None:
-    """A message is dropped when the random draw is below *dropout_rate*."""
+def test_edge_drops_message() -> None:
+    """A request is dropped when `uniform < dropout_rate`."""
     env = simpy.Environment()
     edge_rt, rng, store = _make_edge(
         env,
-        uniform_value=0.1,  # < dropout → drop
+        uniform_value=0.1,  # < dropout_rate → drop
         dropout_rate=0.5,
     )
 
@@ -120,8 +153,33 @@ def test_edge_drops_message_when_uniform_below_threshold() -> None:
     edge_rt.transport(state)
     env.run()
 
+    # no delivery
     assert len(store.items) == 0
     last = state.history[-1]
     assert last.component_id.endswith("dropped")
+
+    # RNG calls
     assert rng.uniform_called is True
     assert rng.normal_called is False
+
+    # counter unchanged
+    assert edge_rt.concurrent_connections == 0
+
+
+def test_metric_dict_initialised_and_mutable() -> None:
+    """`enabled_metrics` exposes the default key and supports list append."""
+    env = simpy.Environment()
+    edge_rt, _rng, _store = _make_edge(
+        env,
+        uniform_value=0.9,
+        dropout_rate=0.0,
+    )
+
+    key = SampledMetricName.EDGE_CONCURRENT_CONNECTION
+    assert key in edge_rt.enabled_metrics
+    assert edge_rt.enabled_metrics[key] == []
+
+    # Simulate a collector append
+    edge_rt.enabled_metrics[key].append(5)
+    assert edge_rt.enabled_metrics[key] == [5]
+
diff --git a/tests/unit/runtime/engine/test_server.py b/tests/unit/runtime/engine/test_server.py
index 69292ba..9e7edb1 100644
--- a/tests/unit/runtime/engine/test_server.py
+++ b/tests/unit/runtime/engine/test_server.py
@@ -1,13 +1,18 @@
-"""Unit tests for the concurrent ServerRuntime.
+"""Unit-tests for ServerRuntime concurrency, resource handling and metrics.
 
-The tests create an isolated SimPy environment with a test fixture that sets up:
-* A single ServerRuntime instance.
-* A mock "instant" edge that immediately forwards requests to a sink.
-* A server configuration with 2 CPU cores and 1024 MB of RAM.
-* A single endpoint with a sequence of RAM, CPU, and I/O steps.
+Each test spins up an isolated SimPy environment containing:
 
-This setup allows for precise testing of resource acquisition/release and
-the correct execution of the processing pipeline for a single request.
+* one ServerRuntime
+* one mock edge with zero-latency delivery (InstantEdge)
+* an inbox (simpy.Store) for incoming requests
+* a sink (simpy.Store) that receives the request after the server
+
+The server exposes:
+  RAM = 1024 MB,  CPU cores = 2
+and a single endpoint with the step sequence:
+  RAM(128 MB) ➜ CPU(5 ms) ➜ I/O(20 ms).
+
+All timings are in **seconds** because SimPy's clock is unit-agnostic.
 """
 
 from __future__ import annotations
@@ -21,56 +26,51 @@
     EndpointStepCPU,
     EndpointStepIO,
     EndpointStepRAM,
+    SampledMetricName,
     StepOperation,
-    SystemNodes,
 )
 from app.resources.server_containers import build_containers
 from app.runtime.actors.server import ServerRuntime
 from app.runtime.rqs_state import RequestState
+from app.schemas.simulation_settings_input import SimulationSettings
 from app.schemas.system_topology.endpoint import Endpoint, Step
 from app.schemas.system_topology.full_system_topology import Server, ServerResources
 
 if TYPE_CHECKING:
+
     from collections.abc import Generator
 
 
-# --------------------------------------------------------------------------- #
-# Test Helper: A mock edge that instantly delivers requests to a sink.      #
-# --------------------------------------------------------------------------- #
-class InstantEdge:
-    """A test stub for EdgeRuntime with zero latency and no drops.
 
-    This mock allows us to test the ServerRuntime in isolation, without
-    introducing the complexities of network simulation.
-    """
+# ---------------------------------------------------------------------------#
+# Helpers                                                                    #
+# ---------------------------------------------------------------------------#
+class InstantEdge:
+    """Stub EdgeRuntime with zero latency and no drops."""
 
     def __init__(self, env: simpy.Environment, sink: simpy.Store) -> None:
-        """Initializes the mock edge."""
-        self.env = env
-        self.sink = sink
+        """Attribute"""
+        self._env = env
+        self._sink = sink
 
     def transport(self, state: RequestState) -> simpy.Process:
-        """Immediately puts the state in the sink via a SimPy process."""
-        return self.env.process(self._deliver(state))
+        """Transport function"""
+        return self._env.process(self._deliver(state))
 
     def _deliver(self, state: RequestState) -> Generator[simpy.Event, None, None]:
-        """The generator function that performs the delivery."""
-        yield self.sink.put(state)
+        """Deliver function"""
+        yield self._sink.put(state)
 
 
-# --------------------------------------------------------------------------- #
-# Test Fixture: Creates a standardized ServerRuntime for tests.             #
-# --------------------------------------------------------------------------- #
 def _make_server_runtime(
     env: simpy.Environment,
 ) -> tuple[ServerRuntime, simpy.Store]:
-    """Create a ServerRuntime with a dummy edge and return it and the sink store."""
-    # 1. Define server resources
+    """Return a (ServerRuntime, sink) ready for injection tests."""
+    # Resources
     res_spec = ServerResources(cpu_cores=2, ram_mb=1024)
     containers = build_containers(env, res_spec)
 
-    # 2. Define a single endpoint with a sequence of steps
-    # Order: RAM (instant) -> CPU (5ms) -> I/O (20ms)
+    # Endpoint: RAM → CPU → I/O
     endpoint = Endpoint(
         endpoint_name="/predict",
         steps=[
@@ -89,93 +89,95 @@ def _make_server_runtime(
         ],
     )
 
-    # 3. Create the full server configuration
-    server_cfg = Server(
-        id="api_srv",
-        endpoints=[endpoint],
-        server_resources=res_spec,
-    )
+    server_cfg = Server(id="api_srv", endpoints=[endpoint], server_resources=res_spec)
 
-    # 4. Set up the simulation environment with mock components
     inbox: simpy.Store = simpy.Store(env)
     sink: simpy.Store = simpy.Store(env)
     edge = InstantEdge(env, sink)
 
-    # 5. Instantiate the ServerRuntime
+    settings = SimulationSettings(total_simulation_time=1900, sample_period_s=0.1)
+
     runtime = ServerRuntime(
         env=env,
         server_resources=containers,
         server_config=server_cfg,
-        out_edge=edge,  # type: ignore[arg-type]
+        out_edge=edge,          # type: ignore[arg-type]
         server_box=inbox,
-        rng=default_rng(0),
+        settings=settings,
+        rng=default_rng(seed=0),
     )
     return runtime, sink
 
 
-# --------------------------------------------------------------------------- #
-# Unit Tests                                                                  #
-# --------------------------------------------------------------------------- #
-def test_server_reserves_and_releases_ram() -> None:
-    """Verify that RAM is acquired at the start and fully released at the end."""
+# ---------------------------------------------------------------------------#
+# Tests                                                                      #
+# ---------------------------------------------------------------------------#
+def test_ram_is_released_at_end() -> None:
+    """RAM tokens must return to capacity once the request finishes."""
     env = simpy.Environment()
     server, sink = _make_server_runtime(env)
 
-    # Prepare a request and inject it into the server's inbox.
-    req = RequestState(id=1, initial_time=0.0)
-    server.server_box.put(req)
-
-    # Start the server's dispatcher process and run until all events are processed.
+    server.server_box.put(RequestState(id=1, initial_time=0.0))
     server.start()
     env.run()
 
-    ram_container = server.server_resources["RAM"]
-    # After the request is fully processed, the RAM level must return to its capacity.
-    assert ram_container.level == ram_container.capacity, "RAM must be fully released"
-    # The request should have successfully reached the sink.
-    assert len(sink.items) == 1, "Request should be forwarded to the sink"
+    ram = server.server_resources["RAM"]
+    assert ram.level == ram.capacity
+    assert len(sink.items) == 1
 
 
 def test_cpu_core_held_only_during_cpu_step() -> None:
-    """Verify a CPU core is held exclusively during the CPU-bound step."""
+    """Exactly one core is busy during the CPU-bound window (0 5ms)."""
     env = simpy.Environment()
     server, _ = _make_server_runtime(env)
-    cpu_container = server.server_resources["CPU"]
+    cpu = server.server_resources["CPU"]
 
-    # Inject a single request and start the server.
-    req = RequestState(id=2, initial_time=0.0)
-    server.server_box.put(req)
+    server.server_box.put(RequestState(id=2, initial_time=0.0))
     server.start()
 
-    # The endpoint logic is: RAM (t=0) -> CPU (t=0 to t=0.005).
-    # Run the simulation to a point *during* the CPU step.
-    env.run(until=0.004)
-    # The server has 2 cores. One should be busy.
-    assert cpu_container.level == 1, "One core should still be busy during the CPU step"
+    env.run(until=0.004)  # mid-CPU step
+    assert cpu.level == 1  # 2-1
 
-    # Now, run the simulation past the CPU step's completion.
-    env.run(until=0.006)
-    # The core should have been released immediately after the CPU step.
-    assert cpu_container.level == 2, "Core should be released after the CPU step"
+    env.run(until=0.006)  # after CPU step
+    assert cpu.level == 2  # released
 
 
-def test_server_records_hop_in_history() -> None:
-    """Verify that the request's history correctly records its arrival at the server."""
+def test_ready_and_io_queue_counters() -> None:
+    """ready_queue_len and io_queue_len should toggle as CPU⇄I/O phases alternate."""
     env = simpy.Environment()
-    server, sink = _make_server_runtime(env)
+    server, _ = _make_server_runtime(env)
 
-    # Inject a request and run the simulation to completion.
-    req = RequestState(id=3, initial_time=0.0)
-    server.server_box.put(req)
+    server.server_box.put(RequestState(id=3, initial_time=0.0))
     server.start()
+
+    # 1) before start queues are empty
+    assert server.ready_queue_len == 0
+    assert server.io_queue_len == 0
+
+    # 2) during CPU (0 5ms) ready queue+1
+    env.run(until=0.003)
+    assert server.ready_queue_len == 1
+    assert server.io_queue_len == 0
+
+    # 3) during I/O (5 25ms) ready 0, io+1
+    env.run(until=0.010)
+    assert server.ready_queue_len == 0
+    assert server.io_queue_len == 1
+
+    # 4) completed both back to 0
     env.run()
+    assert server.ready_queue_len == 0
+    assert server.io_queue_len == 0
+
 
-    # The request must be in the sink.
-    assert len(sink.items) == 1, "Request did not reach the sink"
-    finished_req = sink.items[0]
+def test_enabled_metrics_dict_populated() -> None:
+    """ServerRuntime must create lists for every mandatory sampled metric."""
+    env = simpy.Environment()
+    server, _ = _make_server_runtime(env)
 
-    # Check the request's history for a 'Hop' corresponding to this server.
-    assert any(
-        hop.component_type == SystemNodes.SERVER and hop.component_id == "api_srv"
-        for hop in finished_req.history
-    ), "Server hop missing in request history"
+    mandatory = {
+        SampledMetricName.RAM_IN_USE,
+        SampledMetricName.READY_QUEUE_LEN,
+        SampledMetricName.EVENT_LOOP_IO_SLEEP,
+    }
+    assert mandatory.issubset(server.enabled_metrics.keys())
diff --git a/tests/unit/test_health_route.py b/tests/unit/test_health_route.py
deleted file mode 100644
index 57cfe1f..0000000
--- a/tests/unit/test_health_route.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from fastapi.testclient import TestClient
-
-
-def test_health_endpoint_returns_ok(client: TestClient) -> None:
-    """Ensure the /health endpoint returns HTTP 200 and the expected JSON payload."""
-    response = client.get("/health")
-    assert response.status_code == 200
-    assert response.json() == {"status": "ok"}