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@@ -674,4 +1153,4 @@ typing-extensions = ">=4.12.0" [metadata] lock-version = "2.0" python-versions = "^3.12" -content-hash = "ca95f91568cb076386715d183efe27addd2732eb4347c385a0e9f1479d2035d6" +content-hash = "1441494d6783804e41cd06a1a630c2031b47573ba6f28619d4967e27953aff40" diff --git a/pyproject.toml b/pyproject.toml index b4c46ff..1528987 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,7 @@ pydantic-settings = "^2.10.1" pydantic = {extras = ["email"], version = "^2.11.7"} numpy = "^2.3.1" simpy = "^4.1.1" +matplotlib = "^3.10.3" [tool.poetry.group.dev.dependencies] pytest = "^8.4.1" diff --git a/src/app/config/constants.py b/src/app/config/constants.py index 0e40950..74cf0e2 100644 --- a/src/app/config/constants.py +++ b/src/app/config/constants.py @@ -216,9 +216,9 @@ class SamplePeriods(float, Enum): we have to extract a time series """ - STANDARD_TIME = 0.005 # 5 MILLISECONDS + STANDARD_TIME = 0.01 # 10 MILLISECONDS MINIMUM_TIME = 0.001 # 1 MILLISECOND - MAXIMUM_TIME = 0.1 # 10 MILLISECONDS + MAXIMUM_TIME = 0.1 # 100 MILLISECONDS # ====================================================================== # CONSTANTS FOR EVENT METRICS @@ -244,7 +244,7 @@ class AggregatedMetricName(StrEnum): """aggregated metrics to calculate at the end of simulation""" LATENCY_STATS = "latency_stats" - THROUGHPUT_RPS = "throughput_rps" + THROUGHPUT = "throughput_rps" LLM_STATS = "llm_stats" # ====================================================================== @@ -252,7 +252,23 @@ class AggregatedMetricName(StrEnum): # ====================================================================== class ServerResourceName(StrEnum): - """Keys for each server resource type, used when building the container map.""" + """Keys for each server resource type, used when building the container map.""" - CPU = "CPU" - RAM = "RAM" + CPU = "CPU" + RAM = "RAM" + +# ====================================================================== +# CONSTANTS FOR LATENCY STATS +# ====================================================================== + +class LatencyKey(StrEnum): + """Keys for the collection of the latency stats""" + + TOTAL_REQUESTS = "total_requests" + MEAN = "mean" + MEDIAN = "median" + STD_DEV = "std_dev" + P95 = "p95" + P99 = "p99" + MIN = "min" + MAX = "max" diff --git a/src/app/config/plot_constants.py b/src/app/config/plot_constants.py new file mode 100644 index 0000000..efe28b9 --- /dev/null +++ b/src/app/config/plot_constants.py @@ -0,0 +1,47 @@ +"""Dataclass to define a central structure to plot the metrics""" +from dataclasses import dataclass + + +@dataclass(frozen=True) +class PlotCfg: + """Dataclass for the plot of the various metrics""" + + no_data: str + title: str + x_label: str + y_label: str + ready_label: str | None = None + io_label: str | None = None + legend_label: str | None = None + +LATENCY_PLOT = PlotCfg( + no_data="No latency data", + title="Request Latency Distribution", + x_label="Latency (s)", + y_label="Frequency", +) + +THROUGHPUT_PLOT = PlotCfg( + no_data="No throughput data", + title="Throughput (RPS)", + x_label="Time (s)", + y_label="Requests/s", +) + + +SERVER_QUEUES_PLOT = PlotCfg( + no_data="No queue data", + title="Server Queues", + x_label="Time (s)", + y_label="Queue length", + ready_label="Ready queue", + io_label="I/O queue", +) + +RAM_PLOT = PlotCfg( + no_data="No RAM data", + title="RAM Usage", + x_label="Time (s)", + y_label="RAM (MB)", + legend_label="RAM", +) diff --git a/src/app/metrics/analyzer.py b/src/app/metrics/analyzer.py new file mode 100644 index 0000000..00bbd35 --- /dev/null +++ b/src/app/metrics/analyzer.py @@ -0,0 +1,225 @@ +"""Module for post-simulation analysis and visualization.""" + +from __future__ import annotations + +from collections import defaultdict +from typing import TYPE_CHECKING + +import numpy as np + +from app.config.constants import LatencyKey, SampledMetricName +from app.config.plot_constants import ( + LATENCY_PLOT, + RAM_PLOT, + SERVER_QUEUES_PLOT, + THROUGHPUT_PLOT, +) + +if TYPE_CHECKING: + + from matplotlib.axes import Axes + + from app.runtime.actors.client import ClientRuntime + from app.runtime.actors.edge import EdgeRuntime + from app.runtime.actors.server import ServerRuntime + from app.schemas.simulation_settings_input import SimulationSettings + + +class ResultsAnalyzer: + """Analyze and visualize the results of a completed simulation. + + This class holds the raw runtime objects and lazily computes: + - latency statistics + - throughput time series (RPS) + - sampled metrics from servers and edges + """ + + # Class attribute to define the period to calculate the throughput in s + _WINDOW_SIZE_S: float = 1 + + def __init__( + self, + *, + client: ClientRuntime, + servers: list[ServerRuntime], + edges: list[EdgeRuntime], + settings: SimulationSettings, + ) -> None: + """ + Args: + client: Client runtime object, containing RqsClock entries. + servers: List of server runtime objects. + edges: List of edge runtime objects. + settings: Original simulation settings. + + """ + self._client = client + self._servers = servers + self._edges = edges + self._settings = settings + + # Lazily computed caches + self.latencies: list[float] | None = None + self.latency_stats: dict[LatencyKey, float] | None = None + self.throughput_series: tuple[list[float], list[float]] | None = None + self.sampled_metrics: dict[str, dict[str, list[float]]] | None = None + + def process_all_metrics(self) -> None: + """Compute all aggregated and sampled metrics if not already done.""" + if self.latency_stats is None and self._client.rqs_clock: + self._process_event_metrics() + + if self.sampled_metrics is None: + self._extract_sampled_metrics() + + def _process_event_metrics(self) -> None: + """Calculate latency stats and throughput time series (RPS).""" + # 1) Latencies + self.latencies = [ + clock.finish - clock.start for clock in self._client.rqs_clock + ] + + # 2) Summary stats + if self.latencies: + arr = np.array(self.latencies) + self.latency_stats = { + LatencyKey.TOTAL_REQUESTS: float(arr.size), + LatencyKey.MEAN: float(np.mean(arr)), + LatencyKey.MEDIAN: float(np.median(arr)), + LatencyKey.STD_DEV: float(np.std(arr)), + LatencyKey.P95: float(np.percentile(arr, 95)), + LatencyKey.P99: float(np.percentile(arr, 99)), + LatencyKey.MIN: float(np.min(arr)), + LatencyKey.MAX: float(np.max(arr)), + } + else: + self.latency_stats = {} + + # 3) Throughput per 1s window + completion_times = sorted(clock.finish for clock in self._client.rqs_clock) + end_time = self._settings.total_simulation_time + + timestamps: list[float] = [] + rps_values: list[float] = [] + count = 0 + idx = 0 + current_end = ResultsAnalyzer._WINDOW_SIZE_S + + while current_end <= end_time: + while idx < len(completion_times) and completion_times[idx] <= current_end: + count += 1 + idx += 1 + timestamps.append(current_end) + rps_values.append(count / ResultsAnalyzer._WINDOW_SIZE_S) + current_end += ResultsAnalyzer._WINDOW_SIZE_S + count = 0 + + self.throughput_series = (timestamps, rps_values) + + def _extract_sampled_metrics(self) -> None: + """Gather sampled metrics from servers and edges into a nested dict.""" + metrics: dict[str, dict[str, list[float]]] = defaultdict(dict) + + for server in self._servers: + sid = server.server_config.id + for name, values in server.enabled_metrics.items(): + metrics[name.value][sid] = values + + for edge in self._edges: + eid = edge.edge_config.id + for name, values in edge.enabled_metrics.items(): + metrics[name.value][eid] = values + + self.sampled_metrics = metrics + + def get_latency_stats(self) -> dict[LatencyKey, float]: + """Return latency statistics, computing them if necessary.""" + self.process_all_metrics() + return self.latency_stats or {} + + def get_throughput_series(self) -> tuple[list[float], list[float]]: + """Return throughput time series (timestamps, RPS).""" + self.process_all_metrics() + assert self.throughput_series is not None + return self.throughput_series + + def get_sampled_metrics(self) -> dict[str, dict[str, list[float]]]: + """Return sampled metrics from servers and edges.""" + self.process_all_metrics() + assert self.sampled_metrics is not None + return self.sampled_metrics + + def plot_latency_distribution(self, ax: Axes) -> None: + """Draw a histogram of request latencies onto the given Axes.""" + if not self.latencies: + ax.text(0.5, 0.5, LATENCY_PLOT.no_data, ha="center", va="center") + return + + ax.hist(self.latencies, bins=50) + ax.set_title(LATENCY_PLOT.title) + ax.set_xlabel(LATENCY_PLOT.x_label) + ax.set_ylabel(LATENCY_PLOT.y_label) + ax.grid(visible=True) + + def plot_throughput(self, ax: Axes) -> None: + """Draw throughput (RPS) over time onto the given Axes.""" + timestamps, values = self.get_throughput_series() + if not timestamps: + ax.text(0.5, 0.5, THROUGHPUT_PLOT.no_data, ha="center", va="center") + return + + ax.plot(timestamps, values, marker="o", linestyle="-") + ax.set_title(THROUGHPUT_PLOT.title) + ax.set_xlabel(THROUGHPUT_PLOT.x_label) + ax.set_ylabel(THROUGHPUT_PLOT.y_label) + ax.grid(visible=True) + + def plot_server_queues(self, ax: Axes) -> None: + """Draw server queue lengths over time onto the given Axes.""" + metrics = self.get_sampled_metrics() + ready = metrics.get(SampledMetricName.READY_QUEUE_LEN, {}) + io_q = metrics.get(SampledMetricName.EVENT_LOOP_IO_SLEEP, {}) + + if not (ready or io_q): + ax.text(0.5, 0.5, SERVER_QUEUES_PLOT.no_data, ha="center", va="center") + return + + samples = len(next(iter(ready.values()), [])) + times = np.arange(samples) * self._settings.sample_period_s + + for sid, vals in ready.items(): + ax.plot(times, vals, label=f"{sid} {SERVER_QUEUES_PLOT.ready_label}") + for sid, vals in io_q.items(): + ax.plot( + times, + vals, + label=f"{sid} {SERVER_QUEUES_PLOT.io_label}", + linestyle="--", + ) + + ax.set_title(SERVER_QUEUES_PLOT.title) + ax.set_xlabel(SERVER_QUEUES_PLOT.x_label) + ax.set_ylabel(SERVER_QUEUES_PLOT.y_label) + ax.legend() + ax.grid(visible=True) + + def plot_ram_usage(self, ax: Axes) -> None: + """Draw RAM usage over time onto the given Axes.""" + metrics = self.get_sampled_metrics() + ram = metrics.get(SampledMetricName.RAM_IN_USE, {}) + + if not ram: + ax.text(0.5, 0.5, RAM_PLOT.no_data, ha="center", va="center") + return + + samples = len(next(iter(ram.values()))) + times = np.arange(samples) * self._settings.sample_period_s + + for sid, vals in ram.items(): + ax.plot(times, vals, label=f"{sid} {RAM_PLOT.legend_label}") + + ax.set_title(RAM_PLOT.title) + ax.set_xlabel(RAM_PLOT.x_label) + ax.set_ylabel(RAM_PLOT.y_label) + ax.legend() + ax.grid(visible=True) diff --git a/tests/unit/metrics/test_analyzer.py b/tests/unit/metrics/test_analyzer.py new file mode 100644 index 0000000..4aa33b9 --- /dev/null +++ b/tests/unit/metrics/test_analyzer.py @@ -0,0 +1,190 @@ +"""Unit-tests for ``ResultsAnalyzer`` (latency, throughput, plots).""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, cast + +import pytest +from matplotlib.figure import Figure + +from app.config.constants import LatencyKey +from app.metrics.analyzer import ResultsAnalyzer + +if TYPE_CHECKING: + from app.runtime.actors.client import ClientRuntime + from app.runtime.actors.edge import EdgeRuntime + from app.runtime.actors.server import ServerRuntime + from app.schemas.simulation_settings_input import SimulationSettings + + +# ---------------------------------------------------------------------- # +# Dummy objects (test doubles) # +# ---------------------------------------------------------------------- # +class DummyClock: + """Clock with *start* / *finish* timestamps to emulate a request.""" + + def __init__(self, start: float, finish: float) -> None: + """Save *start* and *finish* times.""" + self.start = start + self.finish = finish + + +class DummyClient: + """Emulates ``ClientRuntime`` by exposing ``rqs_clock``.""" + + def __init__(self, clocks: list[DummyClock]) -> None: + """Attach a list of dummy clocks to the stub client.""" + self.rqs_clock = clocks + + +class DummyName: + """Mimic an Enum member that carries a ``.value`` attribute.""" + + def __init__(self, value: str) -> None: + """Store the dummy string *value*.""" + self.value = value + + +class DummyServerConfig: + """Lightweight replacement for the real ``ServerConfig``.""" + + def __init__(self, identifier: str) -> None: + """Expose only the *id* field required by the analyzer.""" + self.id = identifier + + +class DummyServer: + """Stub for ``ServerRuntime`` exposing ``enabled_metrics``.""" + + def __init__(self, identifier: str, metrics: dict[str, list[float]]) -> None: + """Create a fake server with the given *metrics*.""" + self.server_config = DummyServerConfig(identifier) + self.enabled_metrics = { + DummyName(name): values for name, values in metrics.items() + } + + +class DummyEdgeConfig: + """Minified replacement for the real ``EdgeConfig``.""" + + def __init__(self, identifier: str) -> None: + """Expose only the *id* property.""" + self.id = identifier + + +class DummyEdge: + """Stub for ``EdgeRuntime`` exposing ``enabled_metrics``.""" + + def __init__(self, identifier: str, metrics: dict[str, list[float]]) -> None: + """Create a fake edge with the given *metrics*.""" + self.edge_config = DummyEdgeConfig(identifier) + self.enabled_metrics = { + DummyName(name): values for name, values in metrics.items() + } + + +# ---------------------------------------------------------------------- # +# Fixtures # +# ---------------------------------------------------------------------- # +@pytest.fixture +def simple_analyzer(sim_settings: SimulationSettings) -> ResultsAnalyzer: + """ + Analyzer with two synthetic requests (durations 1 s and 2 s) on a + 3-second horizon and **no** sampled metrics. + """ + sim_settings.total_simulation_time = 3 + clocks = [DummyClock(0.0, 1.0), DummyClock(0.0, 2.0)] + client = DummyClient(clocks) + return ResultsAnalyzer( + client=cast("ClientRuntime", client), + servers=[], + edges=[], + settings=sim_settings, + ) + + +# ---------------------------------------------------------------------- # +# Tests for computed metrics # +# ---------------------------------------------------------------------- # +def test_latency_stats(simple_analyzer: ResultsAnalyzer) -> None: + stats = simple_analyzer.get_latency_stats() + assert stats[LatencyKey.TOTAL_REQUESTS] == 2.0 + assert stats[LatencyKey.MEAN] == pytest.approx(1.5) + assert stats[LatencyKey.MEDIAN] == pytest.approx(1.5) + assert stats[LatencyKey.MIN] == pytest.approx(1.0) + assert stats[LatencyKey.MAX] == pytest.approx(2.0) + assert stats[LatencyKey.P95] == pytest.approx(1.95, rel=1e-3) + + +def test_throughput_series(simple_analyzer: ResultsAnalyzer) -> None: + timestamps, rps = simple_analyzer.get_throughput_series() + assert timestamps == [1.0, 2.0, 3.0] + assert rps == [1.0, 1.0, 0.0] + + +def test_sampled_metrics_empty(simple_analyzer: ResultsAnalyzer) -> None: + assert simple_analyzer.get_sampled_metrics() == {} + + +# ---------------------------------------------------------------------- # +# Tests for plotting methods # +# ---------------------------------------------------------------------- # +def test_plot_latency_distribution(simple_analyzer: ResultsAnalyzer) -> None: + fig = Figure() + ax = fig.subplots() + simple_analyzer.process_all_metrics() + simple_analyzer.plot_latency_distribution(ax) + assert ax.get_title() == "Request Latency Distribution" + + +def test_plot_throughput(simple_analyzer: ResultsAnalyzer) -> None: + fig = Figure() + ax = fig.subplots() + simple_analyzer.process_all_metrics() + simple_analyzer.plot_throughput(ax) + assert ax.get_title() == "Throughput (RPS)" + + +def test_plot_server_queues_with_data(sim_settings: SimulationSettings) -> None: + sim_settings.total_simulation_time = 3 + client = DummyClient([]) + server = DummyServer("srv1", {"ready_queue_len": [1, 2, 3]}) + edge = DummyEdge("edge1", {}) + analyzer = ResultsAnalyzer( + client=cast("ClientRuntime", client), + servers=[cast("ServerRuntime", server)], + edges=[cast("EdgeRuntime", edge)], + settings=sim_settings, + ) + + fig = Figure() + ax = fig.subplots() + analyzer.process_all_metrics() + analyzer.plot_server_queues(ax) + assert ax.get_title() == "Server Queues" + legend = ax.get_legend() + assert legend is not None + texts = [t.get_text() for t in legend.get_texts()] + assert "srv1 Ready queue" in texts + + +def test_plot_ram_usage_with_data(sim_settings: SimulationSettings) -> None: + sim_settings.total_simulation_time = 3 + client = DummyClient([]) + edge = DummyEdge("edgeA", {"ram_in_use": [10.0, 20.0]}) + analyzer = ResultsAnalyzer( + client=cast("ClientRuntime", client), + servers=[], + edges=[cast("EdgeRuntime", edge)], + settings=sim_settings, + ) + + fig = Figure() + ax = fig.subplots() + analyzer.process_all_metrics() + analyzer.plot_ram_usage(ax) + assert ax.get_title() == "RAM Usage" + legend = ax.get_legend() + assert legend is not None + texts = [t.get_text() for t in legend.get_texts()] + assert "edgeA RAM" in texts