Linux Performance Tuning: Optimizing System Performance
Performance tuning improves system responsiveness. This guide covers key tuning areas.
# /etc/sysctl.conf
# Network tuning
net.core.rmem_max = 16777216
net.core.wmem_max = 16777216
net.ipv4.tcp_rmem = 4096 87380 16777216
net.ipv4.tcp_wmem = 4096 65536 16777216
# File system tuning
vm.swappiness = 10
vm.dirty_ratio = 15
vm.dirty_background_ratio = 5
# Apply changes
sudo sysctl -p
# Check current scheduler
cat /sys/block/sda/queue/scheduler
# Set scheduler (for SSDs)
echo deadline > /sys/block/sda/queue/scheduler
# /etc/security/limits.conf
* soft nofile 65536
* hard nofile 65536
* soft nproc 32768
* hard nproc 32768
# CPU and memory
top
htop
# I/O
iostat -x 1
# Network
iftop
- Monitor before tuning
- Change one parameter at a time
- Test changes
- Document changes
- Baseline performance
Tune Linux systems by adjusting kernel parameters, I/O schedulers, and resource limits. Monitor and test changes.
For Linux Performance Tuning: Optimizing System Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Linux Performance Tuning: Optimizing System Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Linux Performance Tuning: Optimizing System Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Linux Performance Tuning: Optimizing System Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
