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Find the delay’s phase before changing a resource value. First determine whether the pod is waiting for placement, scheduled but blocked by CPU, memory or I/O pressure, or running application initialization that probes misinterpret. On Kubernetes running on Linux, correlate pod events and timestamps with requests, limits, cgroup behavior, usage metrics, pressure signals, probe results, rollout activity and (only when relevant) GPU device visibility. This sequence identifies the layer that needs attention instead of turning every slow start into a larger CPU or memory allocation.
Scope: what this method can and cannot tell you
The commands and enforcement details below are for Kubernetes, with Linux cgroups and kubelet metrics where specified. Other orchestrators and container runtimes may expose different states, counters or probe semantics. Treat the workflow as a model to adapt, not proof that every platform behaves identically.
A useful distinction is between four clocks:
- Placement clock: time from pod creation until a scheduler assigns it to a node.
- Container clock: image retrieval, filesystem setup, runtime hook work and process creation.
- Application clock: model or tool loading, configuration, migrations, cache warming and server startup.
- Service clock: the point at which readiness succeeds and the first task completes.
Record these timestamps for both a slow and a healthy start under comparable node and image conditions. This timeline is a diagnostic practice; Kubernetes documentation does not define a universal decomposition of startup time.
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1. Establish whether the pod is unscheduled or initializing
Inspect status, placement and events
Start with the pod and its events before looking at application logs:
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kubectl get pod <pod> -n <namespace> -o wide
kubectl describe pod <pod> -n <namespace>
kubectl get events -n <namespace> --sort-by=.lastTimestampA FailedScheduling event that cites insufficient CPU or memory means the scheduler found no node satisfying the pod’s requests and placement constraints. Kubernetes records such events when no feasible node is available. That is a capacity or placement problem, not evidence that the agent’s initialization code is slow. Review node allocatable resources, taints, affinities, topology constraints and competing workloads before touching probes.
If the pod has a node assignment and a container state such as Waiting, ContainerCreating or Running, the scheduler has already made its decision. Examine image-pull events, runtime errors, process-start timestamps and application logs to separate container setup from application work. A pod can be scheduled promptly yet spend minutes loading a model or waiting on storage.
Build a startup timeline
For each attempt, record:
- Pod creation timestamp.
- Scheduling and node-assignment timestamp from pod status and events.
- Image-pull and container-start timestamps when reported.
- The main process start time from the container log or an application metric.
- Startup-probe success, readiness transition and first successful task.
Compare a slow attempt with a healthy attempt on the same image and similar node conditions. If only the placement interval grows, investigate capacity. If placement is stable but process start or readiness varies with node load, investigate contention. If process start is stable but readiness is late, inspect initialization work and probe policy.
2. Separate requests, limits and observed pressure
What each resource setting does
Kubernetes uses requests when scheduling a pod. A node must have enough allocatable requested CPU and memory, subject to other constraints, for the pod to fit. Limits are enforcement ceilings applied by the kubelet and container runtime through the operating system; on Linux, cgroups provide that control. A configured value is therefore not the same thing as measured consumption.
Check the workload definition and the node’s capacity together:
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kubectl get pod <pod> -n <namespace> -o jsonpath='{.spec.containers[*].resources}'
kubectl describe node <node>Then compare those settings with current usage and restart history. The Kubernetes resource-management documentation explains requests, limits, usage and cgroup enforcement.
| Observation | What it establishes | What it does not establish |
|---|---|---|
| Unschedulable event citing insufficient CPU or memory | No eligible node currently satisfies the pod’s requests and constraints. | It does not show that the application needs more CPU or memory while running. |
| CPU usage near a limit with throttling or latency | The cgroup ceiling may be restricting runnable work. | High usage alone is not proof of contention; correlate with throttling and latency. |
| Memory working set near a limit, OOM termination or restarts | The container may be constrained by its memory cgroup. | It does not identify a leak without heap, allocation or restart evidence. |
| Low container usage but high node pressure | Another workload or node subsystem may be delaying the pod. | It does not prove the agent is correctly sized. |
Memory-backed temporary storage
A memory-backed emptyDir consumes memory charged to the pod. It can grow until the pod memory limit; when no memory limit is set, it may consume available node memory. Include shared-memory files, caches and temporary artifacts in the memory budget rather than comparing only the application heap.
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Measure usage and pressure, not just utilization
Use the Metrics API or your monitoring system when available, and inspect container restarts, throttling counters, eviction messages and node conditions. A utilization percentage is a workload level; pressure indicates that tasks are actually waiting for a resource.
Linux Pressure Stall Information (PSI), when enabled by the kernel and supported by the Kubernetes version, reports time tasks are stalled for CPU, memory or I/O. Kubernetes documents kubelet PSI collection at node, pod and container levels and exposes it through kubelet/cAdvisor metrics. The documented Summary API can be queried through the Kubernetes API proxy, for example:
kubectl get --raw '/api/v1/nodes/<node>/proxy/stats/summary'Inspect the CPU, memory and I/O PSI fields for the affected container and compare them with startup latency and probe timestamps. Endpoint availability and field support depend on cluster version, kernel PSI configuration and kubelet settings, so verify those prerequisites instead of assuming PSI exists.
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3. Test CPU, memory and I/O hypotheses separately
CPU scheduling or throttling
When CPU PSI or throttling rises during initialization, check whether a strict CPU limit is clipping short startup bursts or whether other workloads are consuming the node. A higher request can improve placement on a less-busy node; a higher limit can permit more burst execution, but either change should follow evidence. Increasing a request also reserves more capacity and can make scheduling harder.
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Correlate memory PSI, cgroup usage, OOM-kill events, eviction signals and restart reasons. A limit increase can prevent an avoidable cgroup kill, while a request increase affects placement. If usage is stable but the node is under memory pressure, moving or reducing competing workloads may address the delay more effectively than changing the agent’s limit.
Storage and I/O waits
Model loading, image extraction, cache construction and log-heavy startup can be I/O-bound even when CPU and memory look adequate. Check node disk latency and utilization, container filesystem activity and application logs around file reads. Google Cloud’s GKE CrashLoopBackOff guidance identifies high disk I/O as a cause of delayed container startup, application initialization and probe timeouts. This is provider-specific guidance, but the diagnostic principle applies wherever equivalent disk metrics exist.
4. Make probes match real initialization
Use startup, readiness and liveness for different jobs
Kubernetes defines three distinct controls:
- Startup probe: gates the beginning of health checking for a slow-starting container. Until it succeeds, liveness and readiness probes do not run.
- Readiness probe: decides whether the pod receives traffic. A failed readiness check removes the pod from service without restarting the container.
- Liveness probe: detects a running container that should be restarted. Repeated failures can therefore create a restart loop.
Kubernetes documentation states: “For containers that need to work on loading large data, configuration files, or migrations during startup, consider using a startup probe.” See Liveness, Readiness, and Startup Probes for the exact semantics.
Estimate a startup budget from observations
Set the startup probe’s periodSeconds, timeoutSeconds and failureThreshold so their effective window covers normal initialization under expected contention. For example, a 10-second period and 30-failure threshold allows roughly five minutes before startup failure, subject to probe scheduling and implementation details. Use your measured worst-case startup plus a justified margin; do not copy that example as a universal setting.
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startupProbe:
httpGet:
path: /startup
port: 8080
periodSeconds: 10
timeoutSeconds: 3
failureThreshold: 30
readinessProbe:
httpGet:
path: /ready
port: 8080
periodSeconds: 5
timeoutSeconds: 2
failureThreshold: 3
livenessProbe:
httpGet:
path: /health
port: 8080
periodSeconds: 10
timeoutSeconds: 2
failureThreshold: 3The paths and values are an illustrative pattern, not a recommendation for every agent. Ensure the startup endpoint represents completed initialization, readiness reflects ability to accept work, and liveness is independent of transient dependency failures that should not restart the process.
Account for probe overhead and false failures
During CPU or I/O pressure, an otherwise healthy process may miss a probe deadline. That timeout is evidence of insufficient response time under the observed conditions, not proof of an irreparable application defect. Conversely, an expensive health handler can create the pressure it is meant to detect.
Exec probes create and fork a process at every check. Kubernetes warns that frequent exec checks across a high pod density can add CPU overhead. Incorrect readiness implementations can also permit an ever-growing process population and resource starvation. Review probe type, interval, timeout, failure threshold and handler cost together with logs and pressure metrics.
5. Interpret autoscaling without mistaking startup for steady state
The Horizontal Pod Autoscaler documentation describes a default CPU initialization period of five minutes. Early CPU samples are disregarded unless the pod is Ready and the sample was taken entirely while Ready. This prevents a startup spike from immediately driving scaling decisions, but five minutes is a documented default, not a universal ideal.
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Make startup and readiness behavior reflect the point at which the agent has finished its initialization spike, then tune the HPA initialization period to measured startup duration. Otherwise, a model-loading burst can distort autoscaling, while an overly long period can delay a legitimate response to sustained load. See Kubernetes Horizontal Pod Autoscaling guidance.
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6. Check rollout surges and cluster-wide timing
A deployment that creates many pods simultaneously can saturate image registries, node disks, CPU, memory or network paths. GKE guidance lists large deployment bursts as a cause of delayed startup and recommends staggering deployments or reconfiguring or scaling nodes when appropriate. Compare the slow pod’s start with rollout timestamps and the number of concurrently pending or initializing pods.
Possible controls include reducing a deployment’s surge, lengthening rollout intervals, pre-pulling images where your platform supports it, or adding capacity. Each has a trade-off: slower rollouts extend release time, while added capacity increases cost and does not fix an application-level initialization bottleneck.
7. Use a separate branch for GPU device access
Only take this branch when the agent requests a GPU. Verify from inside the container that the expected device nodes and user-space libraries are visible, and compare runtime, driver and cgroup configuration between healthy and failing nodes.
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8. Choose remediation by symptom and layer
| Observed symptom | Layer addressed | Evidence to collect first | Trade-off or scope |
|---|---|---|---|
FailedScheduling with insufficient CPU or memory |
Scheduling capacity or placement | Pod requests, node allocatable resources, taints, affinities and concurrent pending pods | Larger requests reserve more capacity and may reduce the set of nodes that can fit the pod. |
| CPU throttling or CPU PSI during initialization | CPU limit or node contention | CPU limit, throttling counters, CPU PSI, node load and startup latency | Raising a limit permits bursts but does not create node capacity; changing a request changes placement and reservation. |
| Memory PSI, OOM kills or evictions | Memory limit, request or node pressure | Cgroup usage, restart reason, memory PSI, node conditions and temporary-storage use | More memory can increase cost and still leave a leak or competing workload unresolved. |
| High disk latency or I/O PSI | Storage path or rollout concurrency | Node disk metrics, image and model-read logs, concurrent pod creation | Faster or additional storage may be platform-specific; staggering rollouts slows deployment. |
| Probe failures during otherwise recoverable load | Probe policy and handler | Probe timings, endpoint latency, startup duration and pressure metrics | Longer thresholds delay detection of genuine failures; expensive exec checks add work. |
| GPU library or NVML initialization error | Device injection, runtime, driver or cgroup configuration | Requested resources, device nodes, library versions and node-to-node configuration | Mitigations apply only to GPU workloads and may require runtime or security-policy changes. |
| Many pods slow only during releases | Deployment surge and shared infrastructure | Rollout timestamps, pending pods, image-pull time, node CPU, memory and disk metrics | Lower surge improves stability but lengthens rollout; scaling nodes has ongoing cost. |
9. Put agent-specific latency claims in context
The arXiv preprint AgentCgroup: Understanding and Controlling OS Resources of AI Agents reports that OS-level execution—tool calls, container and agent initialization—accounts for 56–74% of end-to-end task latency — AgentCgroup authors, 2026 (arXiv preprint; abstract; inconsistent with the analysis section’s 55–60% OS-level figure). The analysis section instead reports OS-level execution at 55–60% and LLM reasoning at 40–45%.
These ranges conflict within the same paper. Treat them as that preprint’s measurements, not a universal startup percentage, official operations standard or prediction for your deployment. The paper is useful motivation to measure operating-system work, but your own timeline, pressure signals and probe data must establish where time is spent. Read the source at arXiv and preserve the section attribution whenever quoting a range.
10. A repeatable incident worksheet
For every slow-start incident, save one record containing:
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- Phase: creation, scheduling, image/container setup, process initialization, readiness or first task.
- Configuration: CPU and memory requests and limits, memory-backed volumes, probe definitions and HPA settings.
- Symptoms: events, restart reason, probe response times, application logs and first-task latency.
- Pressure: CPU, memory and I/O usage; throttling; PSI where supported; node conditions; disk latency.
- Concurrency: rollout activity, pending pods, image pulls and major neighboring workloads.
- Special branch: GPU device and library visibility only if the pod requests a GPU.
- Change and result: one remediation, its expected mechanism, measured outcome and any operational cost.
Change one layer at a time when possible. A before-and-after comparison under similar node and rollout conditions is stronger than a single high-utilization snapshot, and it prevents a probe change from being mistaken for a resource fix.
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