> ## Documentation Index
> Fetch the complete documentation index at: https://docs.domino.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Use execution monitoring dashboards

Use these dashboards to monitor workload execution, spot performance issues early, and optimize performance across your deployment.

## Execution health and status

Use these dashboards to monitor workload execution, spot issues early, and optimize performance across your deployment.

**Panels:** Active Executions, Success Rate, Execution Failures

* **Key metrics**

  * Execution status breakdown (Running, Pending, Preparing, Queued, Finishing)

  * Success rate over 30-minute intervals

  * Failure types (System vs User)

* **What to watch**

  * High Pending: Resource bottlenecks

  * Long Preparing: Image pull or volume mount delays

  * Success rate \<85%: Immediate investigation needed

  * Growing Queued: Scheduler issues

* **Targets**

  * Success rate: 95–100% (healthy), 85–95% (watch), \<85% (investigate)

  * Pending time: \<5 minutes

## Startup performance and timing

Check workload status and failure trends in real time. Use this dashboard to detect execution problems and track system health.

**Panels:** Time to Available, Startup Duration, Time in Checkpoint

* **Phases**

  * NodeAssigned: Resource allocation

  * ImagesPulled: Container setup

  * VolumesMounted: Storage readiness

  * FilesPrepared: Git/file staging

  * ExecutionAvailable: Ready state

* **Optimization tips**

  * Delay in NodeAssigned: Scale node pools

  * Bottlenecks in ImagesPulled: Use smaller images

  * Slow VolumesMounted: Investigate storage

  * Lag in FilesPrepared: Check repo size or network

## Resource capacity and scaling

Track resource availability and scaling behavior. Use this dashboard to adjust pool sizes and avoid resource constraints.

**Panels:** Node Pool Size, Resource Utilization

* **Indicators**

  * Pool size trends

  * Resource usage per pool

  * Auto-scaling effectiveness

* **Strategies**

  * Overprovision spare nodes for fast start

  * Tune pool size for cost/performance balance

  * Adjust scaling thresholds

## Image pull performance

Monitor how long container images take to download. Use this data to optimize images and reduce start up delays.

**Panels:** Compute Environment Pull Statistics

* **Metrics**

  * Pull count per image

  * Average pull duration

* **Performance thresholds**

  * \<30 sec: Optimal

  * 30–120 sec: Investigate

  * 120 sec: Action required

* **Improvements**

  * Use optimized, smaller container images

## Next steps

* [Work with model endpoint monitoring dashboards](/cloud/admin/operations/grafana-workload-monitoring/model-endpoint-monitoring) - Monitor the health, reliability, and performance of your model APIs to detect issues quickly and improve model serving.

* [Run data plane agent monitoring dashboards](/cloud/admin/operations/grafana-workload-monitoring/data-plane-agent-monitoring) - Track the health and performance of Domino Data Plane Agents to monitor execution management and Kubernetes resource activity.