What are the best practices for IT infrastructure performance tuning?

Performance tuning spans every layer of infrastructure. Below are the practices that matter most in production.

1. Datacenter Optimization

Power and Cooling: Run hot/cold aisle containment. Monitor inlet temperatures and humidity at the rack level; thermal throttling starts before you see alerts.
Network Fabric: Segment traffic with VLANs and apply QoS for storage, management, and tenant workloads. Use 25/100 GbE uplinks for east-west traffic; avoid oversubscribing spine links.
Physical Maintenance: Inspect cabling, transceivers, and PDU connections quarterly. Label everything; unlabeled fiber is a future outage.

2. Storage Performance

Tiering: Place active datasets on NVMe or SSD; archive to HDD or object storage. Automate tiering with policies, not manual moves.
RAID: Use RAID 10 for write-heavy, latency-sensitive workloads. RAID 5/6 carry a write penalty — avoid for databases or VM boot volumes.
IOPS and Latency: Track both. High IOPS with high latency means queue depth issues. Tune cache ratios, block sizes (typically 4K–64K), and queue depths per workload.
Transport: NVMe-oF (TCP or RoCE) or Fibre Channel for block; NFSv4.1 or SMB3 for file. Match MTU (9000) end-to-end for Jumbo Frames.

3. Backup and Recovery

Incremental Forever: Synthetic fulls reduce backup windows and storage. Verify changed-block tracking works on your hypervisor/storage array.
Replication and Snapshots: Async replication for RPO < 15 min; sync only where required. Snapshots are not backups — replicate them off-array.
Test Restores: Schedule quarterly full restores of critical systems. Document RTO/RPO results.
Deduplication and Compression: Enable on backup targets. Expect 10:1–20:1 for virtualized workloads; less for encrypted or already-compressed data.

4. Server Performance

Right-Size Resources: Monitor CPU ready time, memory ballooning, and disk latency. Over-provisioned VMs waste capacity; under-provisioned ones cause latency spikes.
Firmware and BIOS: Update on a schedule (quarterly minimum). Enable performance profiles (e.g., “Performance Per Watt” vs “Maximum Performance”) per workload.
Hardware Telemetry: Collect IPMI/Redfish metrics — temperature, fan RPM, PSU health, drive SMART. Alert on trends, not just thresholds.

5. Virtualization Best Practices

Resource Management: Monitor overcommitment ratios. CPU overcommit 3:1–4:1 is typical; memory overcommit requires ballooning/compression visibility. Set reservations for latency-sensitive VMs.
Hypervisor Tuning: Keep ESXi/Hyper-V/KVM patched. Disable unused hardware (audio, serial ports). Enable huge pages for large-memory VMs.
Shared Storage: Use SAN/NAS for HA and vMotion. For performance, consider local NVMe with vSAN or similar HCI — lower latency, no fabric hop.
Workload Balancing: Enable DRS (VMware) or equivalent. Set affinity/anti-affinity rules for license-bound or latency-sensitive groups.

6. Windows and Linux Performance

Patch Cadence: Monthly for security; test kernel/firmware updates in staging before production.
Observability: Windows — PerfMon counters (LogicalDisk\Avg. Disk sec/Read, Memory\Available MBytes, Processor\% Privileged Time). Linux — top, iotop, vmstat, pidstat, perf. Use eBPF tools (bpftrace, bcc) for deep dives.
Service Reduction: Disable unused roles (print spooler, Bluetooth, etc.). On Linux, mask unnecessary systemd units.
Filesystem Tuning: XFS or ext4 with noatime,nodiratime; align partitions to 1M. Windows: format ReFS/NTFS with 64K allocation unit for SQL/VM workloads.

7. Kubernetes Optimization

Requests and Limits: Set both. Requests = scheduler guarantee; limits = OOM/CPU throttle boundary. Avoid limit > request for CPU (throttling); memory limit = request for guaranteed QoS.
Cluster Autoscaler: Configure scale-up/down delays. Use node pools per instance type (GPU, high-mem, spot).
CNI: Calico (eBPF dataplane) or Cilium for L3/L4 performance and network policy. Avoid overlay overhead where possible (host-gw, VPC-native).
StorageClasses: Define per backend (NVMe, SSD, HDD). Enable volume expansion. Use CSI drivers with snapshot support.
Monitoring: Prometheus + Grafana + kube-state-metrics. Alert on pod restart rate, CPU throttle %, PVC pending, node pressure conditions.

8. AI Workloads and GPU Tuning

GPU Utilization: nvidia-smi dmon or dcgmi for per-GPU SM, memory, encoder, decoder, NVLink utilization. Target >80% SM occupancy.
Software Stack: Match CUDA/ROCm, cuDNN, NCCL, and driver versions to framework (PyTorch, TensorFlow, JAX). Containerize with NGC or ROCm images.
Parallelism: Data parallelism (DDP/FSDP) scales across GPUs; tensor/pipeline parallelism for model-parallel. Profile with Nsight Systems or ROCm Profiler.
Driver/Firmware: Pin driver versions per cluster. Update during maintenance windows; regressions are common.
Batch Sizing: Maximize batch size within GPU memory. Use gradient accumulation if OOM. Profile memory with torch.cuda.max_memory_allocated().

9. Network Infrastructure

Latency: Enable ECN and PFC for lossless fabrics (RoCEv2). Use cut-through switching where supported. Jumbo Frames (MTU 9000) end-to-end for storage and GPU interconnects.
Bandwidth and QoS: Classify traffic (storage, cluster, tenant). Police non-critical at leaf; prioritize control plane (BGP, health checks).
DNS: Run local recursive resolvers (CoreDNS, Unbound) with caching. Set appropriate TTLs; avoid single-point external resolvers.
Firewall: Place rules in hardware (ASIC) where possible. Order rules: allow established, deny by default, explicit allows. Log only exceptions.

10. Monitoring and Automation

Centralized Metrics: Prometheus (pull) or Datadog/New Relic (push). Standardize labels: cluster, env, team, tier. Retain high-res (15s) for 14 days; downsample for year.
Log Aggregation: Loki, Elasticsearch, or Splunk. Structure logs as JSON. Index labels, not full text. Retain hot 7 days, cold 90 days.
Configuration Management: Ansible for ad-hoc and drift remediation; Terraform for infrastructure. Store secrets in Vault or SealedSecrets — never in git.