Enterprise Monitoring • Kernel Optimization • Observability Engineering • Performance Validation
Real-world CPU, memory, disk I/O, kernel tuning, and observability workflows aligned with Red Hat performance engineering standards.
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This repository demonstrates structured, execution-first Linux performance engineering capability across:
- ✅ CPU scheduling & core-level load balancing
- ✅ Memory pressure analysis & VM tuning
- ✅ Disk I/O bottleneck detection & scheduler optimization
- ✅ Enterprise monitoring automation (sysstat, PCP)
- ✅ Kernel parameter tuning with validation & rollback
- ✅ System diagnostics (dmesg, dmidecode, sosreport)
- ✅ Advanced tracing with SystemTap & eBPF
This is not theoretical study —
it reflects baseline-driven optimization, measurable validation, and automation-focused tuning workflows.
Each lab includes:
- Executed commands
- Automation scripts
- Validation output
- Structured reports (HTML / CSV where applicable)
- Troubleshooting documentation
A structured 20-lab performance engineering program simulating real infrastructure responsibilities such as:
- Production performance troubleshooting
- Capacity planning & regression analysis
- Kernel-level tuning with safe persistence
- Distributed monitoring in enterprise environments
- Deep observability using tracing tools
All labs were executed in controlled RHEL / Ubuntu environments using production-relevant tooling.
This portfolio reflects hands-on capability across the full performance lifecycle:
- Baseline creation & benchmarking methodology
- Bottleneck root cause analysis (CPU, memory, disk, network)
- Kernel parameter optimization & persistence management
- Scheduler tuning & load distribution control
- Historical performance trending & correlation
- Tracing-level debugging with SystemTap & eBPF
- Monitoring automation with cron & systemd
- Executive-ready performance reporting
This represents intermediate → advanced Linux performance engineering capability aligned with Red Hat Specialist standards.
- Linux System Administrators (RHEL / Ubuntu)
- SRE / DevOps / Infrastructure Engineers
- Performance Engineers & Capacity Planners
- Engineers preparing for Red Hat Performance Tuning specialization
- Anyone who wants production-grade tuning + observability workflows
Click a lab title to jump into its folder.
| Lab | Title | Focus Area |
|---|---|---|
| 01 | Introduction to Performance Tuning Concepts | Baseline + tuning fundamentals |
| 02 | Installing Performance Monitoring Tools | Monitoring stack setup + automation |
| 03 | Using top to Analyze System Behavior |
Real-time process diagnostics |
| 04 | Exploring vmstat for Memory & CPU Insights |
Memory pressure + run queue + iowait |
| 05 | Analyzing Disk Performance with iostat |
Storage bottlenecks + scheduler tuning |
🧠 Skills demonstrated
- Baseline-driven tuning methodology
- CPU / memory / disk bottleneck classification
- Scheduler testing + validation workflows
- Scripted monitoring, logging, and reporting
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| Lab | Title | Focus Area |
|---|---|---|
| 06 | Multi-Core CPU Analysis with mpstat |
Core imbalance + affinity tuning |
| 07 | Historical Performance Analysis with sar |
Trending + correlation + reports |
| 08 | Real-Time Monitoring with GNOME System Monitor | Live debugging + memory leak simulation |
| 09 | Power Consumption Monitoring with powertop |
Power efficiency + governor profiles |
| 10 | Advanced Distributed Monitoring with PCP | Multi-host monitoring + alerts |
🧠 Skills demonstrated
- Core-level CPU analysis & load balancing
- Long-term trending, regression detection
- Power optimization profiles + automation (systemd)
- Distributed performance monitoring architecture (PCP)
- HTML/CSV reporting + threshold alerting
| Lab | Title | Focus Area |
|---|---|---|
| 11 | Profiling System Hardware with dmesg |
Boot diagnostics + kernel log triage |
| 12 | Hardware Profiling with dmidecode |
SMBIOS inventory + baseline |
| 13 | System Diagnostics with sosreport |
Support-grade diagnostic bundles |
| 14 | Kernel Parameter Tuning via /proc/sys |
Runtime tuning + persistence |
| 15 | Modifying Kernel Parameters with sysctl |
Profiles + validation + rollback |
🧠 Skills demonstrated
- Hardware + boot-time performance clue extraction
- Structured inventories for capacity planning
- Production-style diagnostic snapshot analysis
- Kernel tuning with persistence + safe rollback controls
- Validation scripting and before/after comparisons
| Lab | Title | Focus Area |
|---|---|---|
| 16 | Kernel Module Parameter Tuning | Kernel/module tuning + benchmarking |
| 17 | Advanced Process Analysis with ps |
Process lifecycle monitoring + RCAs |
| 18 | SystemTap Kernel Performance Analysis | Kernel tracing + latency insights |
| 19 | eBPF Performance Analysis | Modern tracing + syscall analytics |
| 20 | Comprehensive Performance Tuning Review | End-to-end optimization workflow |
🧠 Skills demonstrated
- Kernel-aware tuning with safe persistence
- Process-level performance debugging at scale
- SystemTap probes: latency, syscall tracing, bottleneck detection
- eBPF/BCC tooling: syscall + DNS latency profiling and analytics
- Full lifecycle performance engineering: baseline → optimize → validate → automate
Click to expand
top,htop,vmstat,iostat -x,mpstat,sar,iotop,nethogs,uptime
stress,stress-ng,dd,fio,iperf3
sysctl,/proc/sys,/sys, CPU governors, I/O schedulers (mq-deadline,bfq,kyber,none)ethtool, TCP tuning (BBR, buffers, backlog)
dmesg,dmidecode,sosreport
- Performance Co-Pilot (PCP):
pmcd,pmlogger,pmie,pmchart
- SystemTap
- eBPF (BCC tools):
syscount,gethostlatency,opensnoop,execsnoop
- Bash scripting, Python 3
- cron pipelines, log rotation
- systemd services
- HTML/CSV report generation
Red-Hat-Certified-Specialist-in-Performance-Tuning/
├── 🔹 Foundations – Baselines & Core Monitoring (Labs 1–5)
├── 🔹 Enterprise Monitoring & Optimization (Labs 6–10)
├── 🔹 Diagnostics & Kernel Tuning (Labs 11–15)
├── 🔹 Advanced Performance Engineering (Labs 16–20)
└── README.md
Each lab follows a consistent professional structure:
labXX-<lab-name>/
├── README.md # Objectives, theory, step-by-step guide
├── commands.sh # Executed commands (copy/paste runnable)
├── output.txt # Real command outputs (validation evidence)
├── troubleshooting.md # Common issues & fixes
├── interview_qna.md # Performance tuning Q&A (where applicable)
└── scripts/ # Automation & benchmarking scripts
This ensures:
- ✅ Reproducibility
- ✅ Structured validation
- ✅ Automation-focused workflow
- ✅ Production-style documentation discipline
After completing all 20 labs, this repository demonstrates the ability to:
- Establish structured performance baselines and detect regressions
- Perform CPU core-level analysis and load imbalance debugging
- Diagnose memory pressure, swap behavior, and cache inefficiencies
- Engineer disk I/O optimization using scheduler benchmarking
- Correlate CPU, memory, disk, and network metrics for root cause analysis
- Implement safe kernel tuning with persistence, rollback, and validation
- Deploy enterprise monitoring pipelines (sysstat + PCP)
- Conduct deep system tracing using SystemTap and eBPF
- Produce executive-ready performance reports (HTML / CSV)
- Execute full lifecycle tuning: Baseline → Analyze → Optimize → Validate → Automate
This reflects practical performance engineering capability — not theoretical exercises.
These labs simulate realistic enterprise infrastructure scenarios, including:
- Production performance incident investigation
- Capacity planning & regression detection
- Storage subsystem optimization
- Kernel-level troubleshooting in high-load environments
- Distributed monitoring in SRE / NOC environments
- Observability-driven debugging using tracing tools
- Change validation with measurable before/after benchmarking
- Persistent system optimization under enterprise change control
All labs were executed in controlled Linux lab environments designed to mirror production tuning workflows.
This portfolio reflects:
- Red Hat–aligned performance tuning capability
- Infrastructure-level troubleshooting expertise
- Kernel-aware systems engineering
- Automation-first monitoring mindset
- Structured documentation discipline
- Stakeholder-focused reporting standards
It aligns strongly with roles such as:
- Linux Performance Engineer
- Site Reliability Engineer (SRE)
- DevOps / Infrastructure Engineer
- Enterprise Linux Administrator
- Red Hat Performance Specialist Track
All labs were executed in controlled Linux environments designed to simulate realistic enterprise performance engineering workflows.
Each lab follows a structured operational model:
- Baseline Establishment – Capture system metrics under normal load
- Workload Simulation – Generate controlled CPU, memory, disk, or network stress
- Metric Collection – Monitor using sysstat, PCP, perf, and tracing tools
- Bottleneck Identification – Correlate resource metrics to isolate root cause
- Optimization Implementation – Apply kernel, scheduler, or configuration tuning
- Validation & Benchmarking – Measure before/after impact
- Automation & Persistence – Implement cron/systemd for continuous monitoring
This mirrors how performance tuning is conducted in production environments: measured, validated, reversible, and automation-driven.
This heatmap reflects hands-on implementation across 20 labs in:
CPU Tuning • Memory Optimization • Disk I/O Engineering • Kernel Tuning • Observability • Enterprise Monitoring
Exposure bars represent practical implementation depth across labs.
| Skill Area | Exposure Level | Practical Depth | Tools / Frameworks Used |
|---|---|---|---|
| 🧠 CPU Performance Engineering | ██████████ 100% | Core imbalance detection, affinity tuning, governor optimization | top, mpstat, cpupower, taskset |
| 🧮 Memory & VM Optimization | ██████████ 100% | Swappiness tuning, dirty ratios, cache pressure, swap analysis | vmstat, free, /proc, sysctl |
| 💽 Disk I/O & Scheduler Tuning | ██████████ 100% | Queue depth analysis, await vs %util, scheduler benchmarking | iostat -x, fio, mq-deadline, bfq |
| 📈 Baseline & Benchmarking Methodology | ██████████ 100% | Before/after validation, stress simulation, regression detection | stress-ng, perf, sar |
| 🛰 Enterprise Monitoring Automation | █████████░ 90% | Historical trending, alerting, multi-host monitoring | sysstat, PCP (pmcd, pmlogger, pmie) |
| 🔍 Kernel Diagnostics & Hardware Profiling | █████████░ 90% | Boot diagnostics, SMBIOS inventory, structured health reports | dmesg, dmidecode, sosreport |
| ⚙️ Kernel Parameter Optimization | ██████████ 100% | Runtime tuning, persistence, rollback controls | /proc/sys, sysctl, systemd |
| 🔬 SystemTap Tracing | █████████░ 90% | Latency probes, syscall tracing, bottleneck identification | SystemTap |
| ⚡ eBPF Observability | █████████░ 90% | Syscall analytics, DNS latency tracking, real-time tracing | BCC tools (syscount, opensnoop) |
| 📊 Reporting & Performance Documentation | ██████████ 100% | HTML/CSV reports, structured comparison logs | Bash, Python, cron |
- ██████████ = Implemented End-to-End with Validation & Automation
- █████████░ = Advanced Practical Implementation with Real Outputs
- ████████░░ = Strong Working Implementation with Applied Context
This heatmap represents program-level performance engineering capability, not isolated commands — covering:
Baseline → Stress → Measure → Tune → Validate → Automate
# Clone the repository
git clone https://github.com/abdulrehman215/Red-Hat-Certified-Specialist-in-Performance-Tuning.git
cd Red-Hat-Certified-Specialist-in-Performance-Tuning
# Open any lab
cd labXX-<topic>
# Review objective & methodology
cat README.md
# Execute structured commands
bash commands.sh
# Review captured outputs / validation
cat output.txtEach lab is self-contained and includes setup, execution steps, automation scripts, validation outputs, benchmarking comparisons, and troubleshooting documentation.
Each lab follows a structured performance engineering model:
- Establish system baseline
- Simulate workload (CPU / Memory / Disk / Network)
- Monitor using sysstat / PCP / tracing tools
- Identify bottlenecks
- Apply tuning (kernel / scheduler / sysctl / governor)
- Validate before vs after impact
- Automate monitoring where applicable
All labs were executed in controlled Linux environments designed to simulate realistic enterprise performance engineering workflows.
Environment characteristics:
- RHEL 8/9 and Ubuntu 20.04 / 22.04 LTS
- Root-level administrative access for kernel tuning & tracing
- Controlled workload simulation (CPU, memory, disk, network stress)
- Benchmarking tools (stress-ng, fio, iperf3, perf)
- Monitoring stack (sysstat, PCP, iostat, vmstat, mpstat)
- Tracing tools (SystemTap, eBPF/BCC)
- Automation via Bash, Python, cron, and systemd services
All outputs were validated using before/after benchmarking, structured logs, and performance comparison reports.
This repository is designed to support:
- Linux performance troubleshooting
- Capacity planning & regression detection
- Kernel parameter optimization
- Storage & CPU scheduling validation
- Distributed monitoring deployment
- Observability-driven root cause analysis
- Red Hat performance tuning preparation
All scripts, configurations, and workflows are intended for authorized lab environments, staging systems, or approved maintenance windows.
All tuning, tracing, and benchmarking demonstrated here were performed:
- In controlled lab environments
- On test systems or authorized infrastructure
- With measurable validation and rollback capability
No uncontrolled production systems were modified.
Always apply proper change management, maintenance windows, backups, and validation testing before tuning live environments.
I also shared this Lab series Portfolio on LinkedIn with a concise portfolio summary, key highlights, and implementation context.
This repository reflects real hands-on Linux performance engineering work —
focused on measurable optimization, structured validation, and automation-driven tuning.
It demonstrates the ability to:
Baseline • Measure • Analyze • Optimize • Validate • Automate
If you're building serious capability in Linux Performance Engineering, SRE, DevOps, or Red Hat performance tuning, this portfolio is designed to reflect production-grade methodology — not theoretical exercises.
If this repository adds value, consider starring it ⭐
Happy tuning & engineering 🚀
Abdul Rehman
Linux Performance Engineering • RHEL • Kernel Tuning • Observability • Automation
