Linux Performance Tuning Mastery
@amitmund
July 09, 2026
Linux Performance Tuning Mastery
The Complete Beginner to Advanced Guide to Linux Performance Engineering, Kernel Optimization, CPU Scheduling, Memory Management, Storage Performance, Network Optimization, eBPF, Profiling, Benchmarking, Capacity Planning, and Production Performance Tuning
Course Goal
This course is designed to take you from absolute beginner to production-ready Linux Performance Engineer, Site Reliability Engineer (SRE), Platform Engineer, Infrastructure Engineer, Cloud Engineer, Kernel Engineer, or Distinguished Systems Engineer.
By the end of this learning track, you will be able to:
- Understand how Linux utilizes hardware resources
- Master CPU, Memory, Disk, Filesystem, and Network performance
- Identify and eliminate system bottlenecks
- Optimize Linux servers for production workloads
- Tune Linux Kernel for high-performance computing
- Analyze performance using eBPF and profiling tools
- Design scalable, highly performant infrastructures
- Troubleshoot production performance issues
- Prepare for SRE, Platform Engineering, Cloud, Kernel, and System Design interviews
Prerequisites
- Linux Mastery
- Linux Networking Mastery
- Linux Observability Mastery
- Bash Scripting
- Computer Architecture Basics
- Operating System Fundamentals
- Docker (Recommended)
- Kubernetes (Recommended)
Course Structure
Module 1 — Performance Engineering Fundamentals
Chapter 1 — Introduction to Performance Engineering
- What is Performance Engineering?
- Why Performance Matters
- Performance Metrics
- Throughput
- Latency
- Scalability
- Availability
- Reliability
- Resource Utilization
Chapter 2 — Linux Performance Architecture
- Kernel Architecture
- Scheduler
- Virtual Memory
- Interrupt Handling
- I/O Stack
- Networking Stack
Chapter 3 — Performance Methodologies
- USE Method
- RED Method
- Golden Signals
- Performance Budget
- Bottleneck Analysis
- Capacity Planning
Chapter 4 — Linux Performance Lifecycle
- Measure
- Analyze
- Optimize
- Validate
- Monitor
Module 2 — CPU Performance
Chapter 5 — CPU Architecture
Chapter 6 — CPU Scheduling
Chapter 7 — Process Scheduling
Chapter 8 — CFS Scheduler
Chapter 9 — CPU Affinity
Chapter 10 — NUMA
Chapter 11 — CPU Cache
Chapter 12 — SMT / Hyper-Threading
Chapter 13 — CPU Frequency Scaling
Chapter 14 — CPU Governors
Module 3 — Memory Performance
Chapter 15 — Memory Architecture
Chapter 16 — Virtual Memory
Chapter 17 — Paging
Chapter 18 — HugePages
Chapter 19 — Transparent Huge Pages
Chapter 20 — Swap
Chapter 21 — Memory Fragmentation
Chapter 22 — OOM Killer
Chapter 23 — Memory Pressure
Module 4 — Storage Performance
Chapter 24 — Storage Architecture
Chapter 25 — Block Devices
Chapter 26 — Filesystems
Chapter 27 — ext4
Chapter 28 — XFS
Chapter 29 — Btrfs
Chapter 30 — ZFS
Chapter 31 — RAID Performance
Chapter 32 — SSD vs HDD
Chapter 33 — NVMe
Module 5 — Filesystem Optimization
Chapter 34 — Mount Options
Chapter 35 — Journaling
Chapter 36 — Inodes
Chapter 37 — File Caching
Chapter 38 — Read Ahead
Chapter 39 — Filesystem Tuning
Module 6 — Network Performance
Chapter 40 — TCP Performance
Chapter 41 — Socket Buffers
Chapter 42 — Congestion Control
Chapter 43 — Receive Side Scaling (RSS)
Chapter 44 — RPS/XPS
Chapter 45 — NIC Offloading
Chapter 46 — MTU Optimization
Chapter 47 — Jumbo Frames
Chapter 48 — TCP sysctl Tuning
Module 7 — Linux Kernel Tuning
Chapter 49 — sysctl
Chapter 50 — Kernel Parameters
Chapter 51 — Scheduler Tuning
Chapter 52 — Interrupt Affinity
Chapter 53 — IRQ Balancing
Chapter 54 — Tickless Kernel
Chapter 55 — Kernel Boot Parameters
Module 8 — Process Optimization
Chapter 56 — nice
Chapter 57 — renice
Chapter 58 — taskset
Chapter 59 — cgroups
Chapter 60 — cpuset
Chapter 61 — Resource Limits
Module 9 — Performance Monitoring Tools
Chapter 62 — top
Chapter 63 — htop
Chapter 64 — vmstat
Chapter 65 — iostat
Chapter 66 — mpstat
Chapter 67 — sar
Chapter 68 — pidstat
Chapter 69 — dstat
Chapter 70 — iotop
Chapter 71 — iftop
Module 10 — Advanced Profiling
Chapter 72 — perf
Chapter 73 — Flame Graphs
Chapter 74 — CPU Profiling
Chapter 75 — Memory Profiling
Chapter 76 — I/O Profiling
Chapter 77 — Lock Contention
Chapter 78 — Scheduling Latency
Module 11 — eBPF Performance Analysis
Chapter 79 — eBPF
Chapter 80 — BCC
Chapter 81 — bpftrace
Chapter 82 — bpftool
Chapter 83 — Dynamic Tracing
Chapter 84 — Kernel Instrumentation
Chapter 85 — XDP Performance
Module 12 — Benchmarking
Chapter 86 — fio
Chapter 87 — stress-ng
Chapter 88 — sysbench
Chapter 89 — iperf3
Chapter 90 — wrk
Chapter 91 — ApacheBench
Chapter 92 — Phoronix Test Suite
Module 13 — Database Performance
Chapter 93 — PostgreSQL Optimization
Chapter 94 — MySQL Optimization
Chapter 95 — Redis Performance
Chapter 96 — Connection Pools
Chapter 97 — Query Optimization
Module 14 — Web Server Performance
Chapter 98 — Nginx Optimization
Chapter 99 — Apache Optimization
Chapter 100 — KeepAlive
Chapter 101 — Worker Models
Chapter 102 — Reverse Proxy Performance
Module 15 — Container Performance
Chapter 103 — Docker Performance
Chapter 104 — OverlayFS
Chapter 105 — Container CPU Limits
Chapter 106 — Memory Limits
Chapter 107 — Storage Drivers
Module 16 — Kubernetes Performance
Chapter 108 — Node Performance
Chapter 109 — Pod Performance
Chapter 110 — Scheduler Performance
Chapter 111 — Resource Requests
Chapter 112 — Cluster Optimization
Module 17 — Cloud Performance
Chapter 113 — AWS EC2 Optimization
Chapter 114 — Azure VM Optimization
Chapter 115 — GCP Compute Optimization
Chapter 116 — Auto Scaling
Chapter 117 — Storage Optimization
Module 18 — Capacity Planning
Chapter 118 — Capacity Forecasting
Chapter 119 — Growth Modeling
Chapter 120 — Resource Planning
Chapter 121 — Load Modeling
Chapter 122 — Horizontal Scaling
Chapter 123 — Vertical Scaling
Module 19 — Performance Troubleshooting
Chapter 124 — CPU Bottlenecks
Chapter 125 — Memory Leaks
Chapter 126 — Disk Bottlenecks
Chapter 127 — Network Bottlenecks
Chapter 128 — Lock Contention
Chapter 129 — Kernel Performance Issues
Chapter 130 — Production Performance Debugging
Module 20 — High Performance Computing (HPC)
Chapter 131 — NUMA Optimization
Chapter 132 — DPDK
Chapter 133 — io_uring
Chapter 134 — RDMA
Chapter 135 — GPU Optimization
Chapter 136 — HPC Linux
Module 21 — Enterprise Performance Engineering
Chapter 137 — SLO-driven Performance
Chapter 138 — SLA Optimization
Chapter 139 — Error Budgets
Chapter 140 — Performance Reviews
Chapter 141 — Cost vs Performance
Module 22 — Automation
Chapter 142 — Bash Automation
Chapter 143 — Python Automation
Chapter 144 — Ansible Performance Automation
Chapter 145 — Continuous Benchmarking
Module 23 — Production Case Studies
Chapter 146 — High-Traffic Websites
Chapter 147 — Financial Systems
Chapter 148 — Streaming Platforms
Chapter 149 — AI/ML Infrastructure
Chapter 150 — Kubernetes Clusters
Module 24 — Interview Preparation
Chapter 151 — Linux Performance Questions
Chapter 152 — SRE Interview Questions
Chapter 153 — Performance Engineering Questions
Chapter 154 — System Design Performance Questions
Chapter 155 — Mock Interviews
Module 25 — Bonus
Chapter 156 — Best Practices
Chapter 157 — Performance Anti-Patterns
Chapter 158 — Performance Checklist
Chapter 159 — Performance Cheat Sheet
Chapter 160 — Future of Linux Performance Engineering
Commands Covered
CPU
- top
- htop
- mpstat
- pidstat
- taskset
- chrt
- nice
- renice
Memory
- free
- vmstat
- numactl
- smem
- slabtop
Storage
- iostat
- iotop
- fio
- lsblk
- blkid
- hdparm
Network
- ss
- ip
- iperf3
- ethtool
- tcpdump
- nload
Profiling
- perf
- bpftrace
- bpftool
- strace
- ltrace
Technologies Covered
Linux Performance
- Scheduler
- CFS
- NUMA
- HugePages
- cgroups
- io_uring
Profiling
- perf
- eBPF
- BCC
- bpftrace
- Flame Graphs
Benchmarking
- fio
- stress-ng
- sysbench
- iperf3
- wrk
- ApacheBench
Containers
- Docker
- containerd
- Podman
Kubernetes
- kubelet
- Scheduler
- Metrics Server
- cAdvisor
Cloud
- AWS
- Azure
- GCP
Every Chapter Includes
Every chapter follows the same professional learning structure:
- Learning Objectives
- Theory
- Internal Working
- Linux Kernel Internals
- CPU Scheduling Diagrams
- Memory Allocation Diagrams
- I/O Stack Flow
- Networking Stack Analysis
- Mermaid Diagrams
- ASCII Diagrams
- Architecture Diagrams
- Performance Flowcharts
- Commands
- Configuration Examples
- Bash Scripts
- Python Examples
- Docker Examples
- Kubernetes Examples
- Cloud Examples
- Benchmark Results
- Performance Tuning Checklist
- Production Optimization Examples
- Common Bottlenecks
- Troubleshooting Guide
- Security Considerations
- Best Practices
- Hands-on Labs
- Mini Projects
- Capstone Projects
- Exercises
- Quiz
- Interview Questions
- Cheat Sheet
- Summary
- References
- Further Reading
Hands-on Labs
- Analyze CPU Bottlenecks
- Tune Linux Scheduler
- Optimize Memory Usage
- Configure HugePages
- Benchmark NVMe Storage
- Optimize Filesystem Performance
- Tune TCP Stack
- Optimize Docker Containers
- Tune Kubernetes Nodes
- Analyze with perf
- Trace Kernel Events using eBPF
- Generate Flame Graphs
- Benchmark with fio and sysbench
- Build an Automated Performance Dashboard
- Optimize a Production Linux Server
Capstone Projects
- Enterprise Linux Performance Optimization Toolkit
- Automated Performance Benchmarking Platform
- High-Performance Kubernetes Cluster
- Enterprise Capacity Planning Dashboard
- Linux Performance Observatory
- eBPF Performance Analyzer
- High-Performance Database Platform
- HPC Linux Cluster Optimization
- AI Infrastructure Performance Platform
- Production Performance Engineering Framework
Research & Documentation
Study and analyze:
- Linux Kernel Documentation
- Brendan Gregg's Performance Engineering Resources
- perf Documentation
- eBPF Documentation
- io_uring Documentation
- fio Documentation
- PostgreSQL Performance Guide
- MySQL Performance Guide
- Google SRE Workbook
- Netflix Performance Engineering Blog
Estimated Course Size
- 25 Modules
- 160 Chapters
- 7,000+ Pages
- 3,500+ Command & Configuration Examples
- 1,500+ Performance & Architecture Diagrams
- 600+ Hands-on Labs
- 200+ Production Performance Case Studies
- Complete Linux Performance Engineering, SRE & System Design Interview Preparation
Final Outcome
After completing this learning track, you will be able to:
- Analyze and optimize every layer of the Linux performance stack, from CPU scheduling to application workloads.
- Tune Linux kernels, filesystems, storage, networking, containers, and Kubernetes for maximum efficiency.
- Use modern observability and profiling tools such as perf, eBPF, Flame Graphs, Prometheus, and Grafana to diagnose production bottlenecks.
- Design scalable, high-performance infrastructures for cloud-native, enterprise, HPC, AI/ML, and distributed systems.
- Confidently perform the responsibilities of a Linux Performance Engineer, SRE, Platform Engineer, Infrastructure Architect, or Distinguished Systems Engineer.
- Successfully prepare for advanced Linux Performance Engineering, Cloud, SRE, and System Design interviews.