Use two benchmark tracks, not one: a controlled compute test that measures useful CPU work inside each sandbox, and a lifecycle test that measures creation, scheduling, readiness, execution, and teardown as concurrency rises. A VM boot time, controller throughput number, or isolated compute score alone cannot tell you how many agent tasks one server can serve. The method below shows how to find that limit, expose its latency cost, and make the result reproducible.
What this benchmark must answer
“CPU performance” can mean several different things in an agent platform. A host may execute code quickly once a sandbox is running but take too long to provision environments. Conversely, a fast controller can create many sandboxes while CPU contention makes each agent task miss its service objective. Treat these as separate measurements.
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- Compute capacity: how much fixed work the host completes per second, and how long each task takes, inside the chosen isolation boundary.
- Per-task service quality: the median and tail completion time at a stated number of concurrent tasks.
- Concurrency capacity: the highest active-task level at which the latency objective is still met.
- Lifecycle capacity: how quickly the platform can create, claim, start, and remove sandboxes under cold starts, bursts, and sustained churn.
Define which of these is the decision you need before selecting a test. If the question is “How many sandboxes can one server run?”, state whether you mean idle environments, active CPU-heavy agents, or a mix. Those are different capacities.
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Keep CPU execution separate from sandbox lifecycle
| Track | What it measures | Primary outputs | What it does not prove |
|---|---|---|---|
| CPU execution | A fixed compute-only workload running inside the host and each sandbox class | Completed work per second, task latency, CPU utilization, throttling, and scaling with concurrency | How quickly a new sandbox becomes ready or how much controller capacity exists |
| Lifecycle and controller | Request-to-ready, scheduling, workload start, execution, and teardown | Creation throughput, claim/readiness latency, queueing, startup and removal tails, warm-pool acquisition time | CPU efficiency of the application once it is running |
| Interference | The same workloads with controlled co-tenant CPU pressure and background noise | Change in throughput and latency distributions by isolation model | A universal ranking of containers, pinned cgroups, or microVMs |
Firecracker’s published CPU figure is explicitly for compute-only guest performance. The Kubernetes SIG Agent Sandbox material, and Intel’s vendor-published CubeSandbox method, focus on lifecycle and scheduling dimensions. Use those ideas together, but do not substitute one track for the other.
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Define the useful unit of work
Choose a workload an agent actually performs
Use a deterministic sequence such as a fixed compile, test suite, data-processing job, or scripted tool call. Keep source files, inputs, tool versions, and output validation constant. An agent benchmark that changes prompts, downloads packages, or waits on an external service is measuring network and application variability as much as CPU.
Record the exact workload version and input hash. If the task has an internal timeout, record it as part of the service objective rather than silently discarding slow runs. A useful unit might be “one validated tool sequence completed” or “one fixed dataset transformed,” not simply CPU time.
State the objective before running
Pick one primary objective and publish the others as supporting results:
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- Latency at target concurrency: task completion time at the number of simultaneous agents your service expects.
- Admissible concurrency: the largest active-task count for which a chosen percentile remains below the service objective.
- Provisioning capacity: requests per second or environments per second at a specified readiness target.
Intel’s example uses a 200-millisecond creation target, but that target belongs to its described benchmark; it is not a general industry SLA. Choose and justify your own readiness objective.
Document the platform and isolation boundary
A benchmark is not portable unless another operator can reconstruct the machine, runtime, and limits. Capture the following with every result:
- CPU model, socket and core topology, SMT policy, frequency or power policy, and available physical cores.
- Memory capacity and pressure, storage type, filesystem, and whether the host is otherwise idle.
- Operating-system release, kernel version, hypervisor or sandbox runtime version, and the commit or release of rolling project documentation used to configure it.
- Guest kernel and root filesystem or container image digest, language and toolchain versions, and workspace contents.
- Per-sandbox vCPU count, memory, CPU quota, shares, cpuset or affinity, and any oversubscription ratio.
- Whether the boundary is a shared host-kernel container, cgroups with pinning, a microVM with a guest kernel, or another design.
- Network, mounted filesystems, image-cache state, and whether workspaces are local, mounted, or remote.
Firecracker documents cgroup CPU quotas and CPU/node affinity for microVMs and notes that oversubscription is an operator choice. Record those settings instead of reporting only the nominal vCPU count.
Control image and workspace state. A warm image cache can remove pull and unpack work; a remote or network-attached workspace can add filesystem latency. Docker’s documentation specifically warns that filesystem passthrough to remote storage adds latency and that sandboxes have separate daemon and image-cache state. If storage can affect the task, benchmark it as a separate path or disclose it as part of the end-to-end result.
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Build the test matrix before collecting numbers
Use a matrix that varies one important factor at a time. At minimum, include a quiet baseline and the deployment configuration you expect to operate.
| Dimension | Example levels to record | Reason |
|---|---|---|
| Isolation | Host process baseline, container, cgroup-pinned container, microVM | Different boundaries respond differently to scheduling and interference. |
| CPU allocation | One vCPU, multiple vCPUs, quota-limited, pinned, or oversubscribed | Nominal allocation does not equal guaranteed execution time. |
| State | Cold image and workspace, warm image cache, warm pool | Provisioning and acquisition paths have different costs. |
| Load shape | Single run, gradual ramp, short burst, sustained churn | Queues and controllers behave differently under each shape. |
| Co-tenancy | Quiet host, representative background CPU load, intended mixed workload | Average results can hide interference and tail latency. |
Do not compare two rows if their workload input, image, CPU limit, or storage path changed at the same time. If a change is intentional, label the result as a configuration comparison rather than an isolation comparison.
Track A: benchmark CPU execution
Establish a quiet baseline
- Prepare the fixed workload and verify its output so a fast but incorrect run cannot count as completed work.
- Run it on the host or an agreed baseline with one active task. Record wall-clock task latency, CPU time, completed work, frequency behavior, and memory pressure.
- Repeat the same workload inside each sandbox class with identical input and an explicitly stated CPU allocation.
- Run enough repetitions to expose run-to-run variation. Publish the count, duration, and any excluded runs with a reason.
Report both throughput and latency. Throughput is completed work divided by elapsed test time; task latency is measured per completion. A system can increase aggregate throughput while making individual agents slower, so neither metric is sufficient by itself.
Increase active concurrency
After the one-task baseline, increase simultaneous active tasks in planned steps. Use small steps near the expected knee and larger steps when the host is lightly loaded. At each level, keep the workload and sandbox limits constant and record:
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- Successful completions, errors, timeouts, and queue depth if available.
- Aggregate throughput and per-task median, p95, and p99 latency, or another disclosed percentile set.
- Host and guest CPU utilization, throttling time, run-queue pressure, memory pressure, and frequency changes.
Mark the first point at which the latency objective is missed and the point at which adding concurrency no longer increases throughput. That interval is more useful than a single “maximum” number: it shows where useful work stops scaling and where service quality starts to fail.
Interpret a guest-versus-host result carefully
The Firecracker project specification states that compute-only guest CPU performance is “> 95%” of equivalent bare-metal performance for its specified setup and marks the integration test as pending. Treat this as a project-published claim tied to its documented conditions, not independent validation or a universal guarantee. If you calculate a ratio, publish the exact workload, vCPU allocation, host, guest image, and concurrency used for both sides.
Track B: benchmark the sandbox lifecycle
Instrument separate timestamps
Do not collapse every event into “startup.” For each request, capture:
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- Request accepted by the control plane.
- Scheduling decision and queue wait.
- Sandbox process or VM creation.
- Claim or readiness signal.
- Workload process start.
- Workload completion.
- Teardown request and confirmed removal.
These timestamps let you distinguish CPU execution from controller delay, image handling, guest boot, and cleanup. Report request-to-ready and ready-to-completion separately; also show end-to-end request-to-completion for users who experience the whole path.
Cold creation test
Start with no reusable environment and a defined image-cache state. Submit a controlled number of requests, wait for readiness, run the fixed task, and remove each sandbox. Measure readiness and teardown distributions, not only averages. A cold result should state whether image pulls, filesystem creation, or workspace setup were included.
Warm-pool acquisition test
Pre-create a pool of idle, ready environments. Then measure the time from claim request to an assigned sandbox and workload start. Keep replenishment visible: a warm pool can make acquisition fast while its refill controller becomes the bottleneck. Report pool size, initial fill method, refill policy, and whether the image and workspace were already cached.
Burst, ramp, and churn tests
Run three distinct shapes:
- Burst: issue many requests over a short interval, then observe queueing and recovery.
- Ramp: increase offered concurrency gradually until throughput plateaus or the readiness objective fails.
- Churn: continuously create, run, and delete environments for a sustained period to expose leaks, fragmentation, and controller backlogs.
The Kubernetes SIG Agent Sandbox guide documents startup, claim, throughput, ramp, burst, churn, and warm-pool scenarios. Its script defaults include 1,000 claims per burst, 1,000 requests per second, 10 bursts, and a warm pool of 1,000; it also describes 400 burst size and 400 requests per second as a conservative starting point. These are documented test defaults and guidance, not observed capacity results.
Measure interference, not just an idle host
Repeat representative CPU and lifecycle points with controlled background load. Use the co-tenant arrangement expected in production, or describe the synthetic load precisely. Compare the same workload at the same offered concurrency with and without the background activity.
Report changes in throughput and the full latency distribution. A small median difference can conceal a large p99 increase. Also watch CPU throttling, scheduler run queues, memory reclaim, and storage wait so you can identify whether the cause is CPU contention or another resource.
A 2025 study listed in the TU Berlin repository examined cgroups and CPU pinning, containers, and Firecracker with a noise generator. Its abstract reports differences in latency distributions and different isolation effects. Use that finding as a reason to test your own interference pattern, not as proof that one runtime is always best. Results depend on topology, limits, kernel, workload, and noise placement.
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Understand the named Firecracker figures—and their limits
| Figure | What it describes | Qualification |
|---|---|---|
| 8 CPU milliseconds | VMM startup to API socket availability | Project specification, accessed 2026; not guest boot or agent readiness. |
| 6–60 milliseconds wall-clock, typically about 12 milliseconds | VMM process startup under documented conditions | Project figure; hardware, software, and configuration matter. |
| At most 5 MiB | VMM thread memory overhead for a stated one-vCPU, 128 MiB microVM configuration | Workload-dependent caveats apply. |
| At most 125 milliseconds | InstanceStart API call to guest /sbin/init for the stated minimal kernel and root filesystem |
Not end-to-end sandbox or task readiness. |
| Greater than 95% | Compute-only guest CPU performance relative to equivalent bare metal | Project-published claim for a specified setup; the integration test is marked pending. |
| 5 microVMs per host core per second | Creation rate for a minimal-kernel, single-core, 128 MiB configuration | Narrow project figure, not general agent capacity. |
| 180 microVMs per second | Example rate on a 36-physical-core host | Illustrative calculation under that same narrow configuration. |
Use these numbers to design hypotheses and sanity checks. Do not turn them into a promise that a production server will sustain the same rate with larger images, remote workspaces, multiple vCPUs, or active agents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Find the concurrency knee
Plot offered concurrency on the horizontal axis and throughput, median latency, p95, and p99 on separate series. Add CPU utilization, throttling, and queue depth as annotations. The “knee” is where additional concurrency produces little or no throughput gain while latency begins to rise sharply.
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Choose an operating limit using your service objective. For example, if p95 completion must remain below a stated threshold, select the highest concurrency whose confidence interval or run-to-run range remains below that threshold. Report the next tested point as well; it shows how quickly the system falls off after the limit.
For lifecycle tests, perform the same analysis with offered creation rate. A controller may sustain high creation throughput while readiness tails grow, or a warm pool may absorb a burst and then fail during refill. State which condition determines admission.
Repetition, variation, and statistical reporting
There is no universal sample count or percentile set in the cited materials. Choose a defensible protocol and disclose it. At each concurrency level, report the number of independent runs, duration, warm-up period, and whether runs were randomized or ordered. Include a confidence or variation method such as a confidence interval, interquartile range, or standard deviation, and explain it.
Never publish only the best run. If you discard a run, record the objective reason—such as a host failure, invalid workload output, or measurement outage—and keep the discarded count visible. A failed run caused by the system under test is a result, not automatically an outlier.
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| Axis | Questions to answer |
|---|---|
| Boundary and threat model | Does the workload share the host kernel, or run with a guest kernel? What isolation assumption is being tested? |
| CPU work | How much validated work per second is completed, and how does per-task latency change with concurrency? |
| Lifecycle | What are creation, readiness, acquisition, and teardown throughput and tails? |
| Scheduling controls | What quota, pinning, affinity, oversubscription, and throttling settings apply? |
| Resource overhead | What memory, storage, image-cache, and workspace costs are included? |
| Interference | How do median and tail results change with the intended co-tenant load? |
| Repeatability | Can another operator reproduce the exact image, kernel, runtime, topology, and limits? |
Do not describe an isolation model as having a fixed, universal performance penalty. Containers, pinned cgroups, and microVMs can behave differently depending on scheduler policy, CPU topology, workload mix, and storage path.
A practical run procedure
- Write the decision statement. Specify throughput, latency, admissible concurrency, or provisioning capacity as the primary outcome.
- Freeze the workload. Version the script, inputs, tools, image, and output validator.
- Capture configuration. Record host topology, OS and kernel, runtime, guest or container image, CPU and memory limits, affinity, storage, network, and cache state.
- Run a quiet single-task baseline. Establish host and sandbox execution results before adding concurrency.
- Run the CPU ramp. Increase active tasks, recording throughput, latency percentiles, resource pressure, throttling, and errors at every step.
- Run lifecycle scenarios. Measure cold creation, warm-pool acquisition, bursts, ramp, and sustained churn with separate timestamps.
- Repeat under interference. Apply representative background CPU pressure and repeat selected baseline, knee, and overload points.
- Analyze the knee. Mark the first missed service objective and the throughput plateau; do not choose a limit from a single metric.
- Publish the complete matrix. Include run counts, durations, variation method, discarded runs, and every configuration needed to reproduce the result.
Common mistakes and how to correct them
Calling boot speed CPU performance
A VMM API-to-init figure measures one lifecycle segment. Correct it by reporting guest startup, readiness, and task execution separately.
Reporting only average latency
Averages hide queue buildup and rare slow tasks. Correct it with median and disclosed tail percentiles, plus the distribution under burst and sustained load.
Using one concurrency point
One point cannot show saturation. Correct it with a ramp, a burst, and churn, then report the knee and the post-knee behavior.
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Two sandboxes with the same vCPU label can receive different CPU time. Correct it by publishing quotas, pinning, affinity, SMT policy, and oversubscription.
Mixing warm and cold states
Warm images and pools remove work that cold creation must perform. Correct it by running and labeling both paths.
Letting storage or network dominate silently
Remote workspaces and image pulls can overwhelm CPU differences. Correct it by recording those paths, controlling cache state, and measuring them separately when necessary.
Generalizing a vendor or project figure
Firecracker’s figures are project specifications with stated conditions; Intel’s comparison is vendor-published and CubeSandbox-specific; Kubernetes SIG measurements describe lifecycle dimensions. Correct attribution and configuration details are part of the result.
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What a publication-quality result should contain
- A one-sentence objective and service target.
- A platform block with CPU topology, OS/kernel, runtime, image, limits, affinity, storage, and cache state.
- A workload block with version, input, validation rule, and duration.
- Separate tables or plots for CPU execution and lifecycle capacity.
- Concurrency ramps showing throughput, median, p95, p99, queue depth, utilization, and throttling.
- Cold, warm-pool, burst, and churn results where those paths exist.
- Quiet-host and co-tenant interference results.
- Run counts, test duration, warm-up treatment, variation or confidence method, and excluded-run reasons.
- A clearly marked admissible-concurrency point and the next overload point.
- Limitations stating which figures are project specifications, vendor methodology, script defaults, or pending validation.
This format lets a reader answer both operational questions: how much CPU work each sandbox can deliver, and how many environments the platform can provision and recycle without violating its service objective.
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