Model virtualization impact using inputs from real tests. Tune weights and see an overall score. Export summaries to share with operations and finance leaders.
| Scenario | Baseline runtime (s) | Virtual runtime (s) | Disk IOPS (base/virt) | Overall overhead |
|---|---|---|---|---|
| Web API smoke test | 85 | 92 | 40,000 / 36,000 | ~9% |
| Database OLTP batch | 300 | 345 | 60,000 / 48,000 | ~18% |
| ETL analytics run | 2,400 | 2,520 | 25,000 / 23,500 | ~6% |
Use paired tests: same dataset, same request mix, and identical host power settings. Record baseline on bare metal, then rerun on the virtual stack with the same CPU pinning, memory limits, and storage queue depth. Capture at least three runs and use the median. A 120s baseline that becomes 132s virtual is a 10.0% time increase, which the calculator treats as overhead for that component. Note firmware, kernel, and driver versions to keep comparisons defensible across quarters.
CPU overhead is most meaningful when workload is fixed. If utilization rises from 55% to 60% for the same throughput, it suggests scheduling cost, interrupt handling, or co‑tenancy contention. Memory overhead appears when guest drivers, page tables, or ballooning increase resident usage; for example, 16GB to 18GB is 12.5%. Use the reserve field to model host services, monitoring agents, and hypervisor housekeeping. Capture CPU steal time; it often explains consolidation slowdowns during peaks.
For storage, combine IOPS loss with latency growth. Dropping from 50,000 to 43,000 IOPS is a 14.0% loss, while 1.2ms to 1.5ms latency is a 25.0% increase; the calculator averages what you provide to avoid overfitting. For network, a throughput dip from 950 to 880Mbps is a 7.4% loss, and a 0.8ms to 1.0ms rise is 25.0%. Track p95 latency separately for critical services.
Weights turn individual overheads into a single planning number. Start with 100% total, then allocate more weight to the resource that limits your SLO. Databases often emphasize disk and memory, while web tiers may emphasize network and CPU. If a metric is unreliable, set its weight to 0 and rely on the remaining signals. The overall overhead is the weighted average plus any reserve you add.
After calculation, the capacity multiplier estimates how much raw capacity you need to deliver the same output. An overall 15% overhead implies a 1.150× multiplier. If a host has 24 cores, effective compute becomes about 20.87 cores. Use the risk band to communicate impact quickly and the suggested headroom to size safely for bursts. Export CSV for spreadsheets and PDF for change reviews and tickets.
Use bare‑metal or the least‑virtualized configuration running the same workload and tuning. Collect median results from multiple runs and keep hardware, drivers, and storage policy identical.
Caching, turbo behavior, NUMA placement, or measurement noise can make the virtual run look faster. Treat small negatives as zero for planning unless you can reproduce the gain consistently.
Prioritize disk latency, disk IOPS, memory usage, and runtime. Assign higher weights to storage and memory, and lower weights to network unless replication traffic dominates.
Start with 2–5% for light hosts. Increase it if you run heavy monitoring, backups, encryption, or frequent live migrations. Validate by observing host CPU when VMs are idle.
Yes, but keep workload output constant. Changing vCPU or memory changes the baseline itself, so run a new baseline for each sizing profile you want to evaluate.
No. It is a planning shortcut. Use it to estimate headroom and cost, then validate with load tests that include failover, peak traffic patterns, and noisy‑neighbor conditions.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.