Why This Topic Matters
NUMA describes systems where CPUs and memory are organized into nodes and memory-access cost differs between local and remote access.
Key Decision Points
Consider NUMA boundaries when sizing large VMs.
Know the physical NUMA topology of the host.
Do not size very large VMs using aggregate resources alone.
Check remote-memory effects during performance analysis.
Architecture and operating model
NUMA effects depend on memory locality, sockets and VM placement. A VM exceeding physical-node capacity may require remote memory or different vNUMA exposure; exact behavior is hypervisor-specific. CPU affinity or pinning is not an automatic improvement for every workload.
- 1Physical NUMA topology
- 2VM CPU/RAM placement
- 3Local and remote access
- 4Application measurements
Design parameters
- Locality
- Compare vCPU and memory placement with physical NUMA boundaries; remote memory can add latency.
- vNUMA and size
- Review VM size, guest support and memory assignment together; extra vCPUs do not guarantee speed.
- Peak workload
- Collect CPU, memory, storage and network measurements for the same period. A single utilization percentage does not determine sizing.
- N-1 capacity
- Calculate capacity after losing one component. Normal spare headroom does not prove sufficient failure-state capacity.
- Compatibility chain
- Review CPU, firmware, drivers, hypervisor and guest-application support together.
- Operating reserve
- Backup, migration, maintenance and growth compete for resources. Do not reserve headroom using averages alone.
Worked example
Assume 128 GB per NUMA node. Examine placement, guest topology and workload latency for a 192 GB VM. Compare a 96 GB test VM using the same dataset while distinguishing reduced-cache effects from locality effects.
Example commands: replace lab values and confirm permissions and software versions before use.
lscpu
numactl --hardware
Troubleshooting
| Observation | Likely cause / distinction | Verification |
|---|---|---|
| RAM is available but latency is high | Remote memory or scheduling placement may be involved. | Compare NUMA placement and p95 latency under equivalent controlled load. |
| Application is slow despite low CPU usage | Storage, locking, hot cores or scheduling waits may be hidden by aggregate CPU usage. | Correlate application latency with core, storage and hypervisor waits. |
| Host overloads after failure | Capacity planning may cover normal placement but omit N-1 conditions. | Measure remaining RAM, CPU and storage latency during a controlled failure. |
| Performance worsened after a change | Drivers, firmware, NUMA placement or resource allocation may have changed. | Compare before and after under equivalent load, changing one variable at a time. |
Acceptance checks
- Validate topology changes using application latency, not CPU percentage alone.
- Test representative peak load together with maintenance tasks.
- Verify service and writes after losing a host or connection.
- Check guest OS and application support against official matrices.
- Validate device, service and storage health after reboot.
- Record raw and usable capacity separately.
Related concepts
NUMA locality
On NUMA systems a processor may access nearby and remote-node memory at different costs. VM vCPU and memory sizes must be assessed against physical node capacity and hypervisor placement. More vCPUs do not always improve performance: reduced locality and scheduling waits can make it worse. Record physical topology, topology exposed to the guest and the workload’s memory behavior together. Compare changes under the same load, using application latency and remote-memory effects rather than CPU percentage alone.
Resource contention
Contention occurs when workloads sharing CPU, memory, storage or a link make each other wait. Apparent spare total capacity can hide a hot core or single-queue bottleneck. Correlate backup jobs, antivirus scans, index maintenance and user traffic on a common timeline. Confirm the bottleneck before adding resources. Run a workload alone and with its usual competitors to separate shared-resource effects, and report peak-hour latency alongside average utilization.
Capacity and usable headroom
Raw capacity is not the capacity available to applications. RAID or erasure coding, filesystems, reserved space, metadata, snapshots and growth headroom are separate deductions. TB and TiB representations also change the displayed number. Write calculations with units, establish protected usable capacity, then subtract operating reserves. Track growth rate as well as current utilization. The projected exhaustion date should leave enough time to procure and deploy additional capacity.
IOPS and block size
IOPS measures operations per second, whereas MB/s measures transferred data. Identical IOPS values produce very different bandwidth at different block sizes. For example, 10,000 operations/s at 4 KiB is approximately 39.1 MiB/s. Read/write mix, random versus sequential access and protection mechanisms affect the result. Measure actual block distributions and concurrency rather than transferring a benchmark result directly to production. High disk IOPS does not prove good application response; latency must be examined alongside it.
Availability versus recovery
High availability aims to keep service running through specified failures with a short interruption; backup recovers lost or corrupted data from an earlier point. A cluster can replicate an accidental deletion to another node. HA therefore does not replace backup. Consider DNS, identity, network, storage and power dependencies together. Successful node failover is insufficient by itself: measure user sessions, application writes and external integrations after the transition as well.
Failover and failback
Failover moves service to another component; failback returns it to the preferred location. Their risks and sequencing may differ. DNS updates, routing, sessions, replication lag and application dependencies determine user interruption. Define failover triggers, false-alarm behavior and approval for returning service. Simply shutting down a VM does not test every failure type. Measure network, storage and management-plane failures separately.
Primary documentation
How This Content Was Prepared
Doz Teknoloji Technical Team
This content is based on the technical evaluation approach used across Doz Teknoloji's enterprise IT practice areas.
Technical criteria + primary sources
The topic was cross-checked against vendor documentation, publications from standards bodies and other relevant official technical sources.
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