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On-Demand CI Runners on Nomad: How Temporal Fits—and What to Verify

Nomad can dispatch CI work and scale runner capacity, while GitHub Actions ephemeral runners handle one job each. The integration and lifecycle role for Temporal require verification.
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Nomad can dispatch CI work and scale the capacity that runs it; GitHub Actions supports ephemeral self-hosted runners that deregister after one job. Temporal may be used as an orchestration layer, but the available official documentation does not establish a ready-made Temporal–Nomad integration or verify its workflow, retry, cancellation, or cleanup behavior. Treat the design below as an architecture pattern, not a prebuilt integration.

What this architecture is meant to do

On-demand CI capacity aims to start runner infrastructure when work arrives and release it when demand falls. In a Nomad-based design, a CI event can trigger a parameterized Nomad job; Nomad places the resulting work, while the separate Nomad Autoscaler can add client nodes when queued work requires more capacity. For GitHub Actions, an ephemeral self-hosted runner can then take one job and be automatically deregistered.

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These are separate capabilities, not a single integrated product feature. The event bridge, runner bootstrap and registration, the signal used to scale, and the lifecycle controller must be implemented and operated as part of the system.

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How a CI job can move through the system

  1. Receive work. A CI event or queue signal identifies work that needs a runner. Decide how the event reaches the orchestration layer and how missed or delayed events will be detected.
  2. Request Nomad work. A parameterized Nomad job can be started with nomad job dispatch. The command creates a distinct dispatched job instance and accepts an idempotency token; its payload limit is 16 KiB. See the Nomad dispatch command reference.
  3. Place the job and supply capacity. Nomad evaluates placement. If available clients cannot satisfy demand, a separately running Nomad Autoscaler can scale client nodes or task-group allocation counts, depending on the configured policy.
  4. Run the CI job. The infrastructure needs to start a runner, make it available to the intended repository or organization, and route the requested job to it. The exact mechanism depends on the runner and deployment design.
  5. Release resources and retain evidence. After work completes, retire the temporary capacity according to the design and preserve runner logs outside the short-lived runner environment for troubleshooting.

What Nomad contributes—and what it does not

Dispatching a job

Parameterized jobs provide a way to create a distinct dispatched instance for a request. The dispatch payload is limited to 16 KiB, so pass only the inputs needed to identify or configure the work; do not treat dispatch as an unrestricted artifact-transfer channel. An ACL-enabled caller needs the dispatch-job capability in the target namespace. These details are in HashiCorp’s dispatch reference.

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Scaling allocations versus scaling clients

Nomad’s job scaling block is an interface for external autoscalers: its policy data is consumed by the autoscaler, and group scaling changes the number of allocations. Scaling client nodes is a different operation: it adds or removes the machines on which Nomad can place work. The job scaling block documentation and the Nomad Autoscaler overview describe these mechanisms. The Autoscaler runs as a separate daemon, not as part of a Nomad Agent.

HashiCorp’s on-demand batch scaling tutorial demonstrates provisioning clients when batch work queues and decommissioning them after the work finishes. HashiCorp warns that the demo infrastructure has billable costs and is not suitable for production as-is; use it as a pattern to evaluate, not a deployment recipe.

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Where Temporal can fit—and what needs verification

Temporal is named in this architecture, but the official Nomad and GitHub sources cited here do not verify Temporal-specific workflow or activity behavior, a direct integration, or guarantees for provisioning, retries, cancellation, and cleanup. Before treating Temporal as the lifecycle controller, verify those details against current Temporal documentation and the implementation you plan to deploy.

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At the design level, an orchestration layer could coordinate the transitions between a CI request, a Nomad dispatch, runner availability, job completion, and resource retirement. Define which system owns each state transition and how it detects a lost event, a dispatch that never becomes runnable, a cancelled CI job, or a runner that disappears. Do not infer that Nomad dispatch or autoscaling supplies those orchestration semantics.

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How to choose a scaling signal

For GitHub Actions, GitHub notes that webhook delivery timeliness can affect autoscaling reliability. It points to Actions Controller or the Scale Set Client for larger-volume scenarios. That guidance does not establish that either option integrates directly with Nomad or Temporal.

Approach What to weigh Best fit to evaluate
Webhook-driven scaling Events can initiate a response promptly, but delayed or missed delivery can affect reliability. The design needs a way to reconcile event-driven requests with actual queued work. Workloads where event handling is manageable and delivery reliability can be monitored.
Scale-set-aware approach GitHub identifies the Actions Controller and Scale Set Client as options for larger-volume scenarios. Their use with Nomad and Temporal is not established by the cited guidance. Higher-volume GitHub Actions environments evaluating GitHub’s documented scaling options and their fit with the surrounding infrastructure.

Reliability, security, and operations to plan for

  • Prevent duplicate requests. Use Nomad’s dispatch idempotency token where suitable, and define how the event bridge handles repeat delivery. The token is a dispatch feature, not proof of end-to-end deduplication across the entire CI lifecycle.
  • Set queue and capacity expectations. A GitHub Actions job with no matching runner can remain queued and fails if it stays queued for more than 24 hours, according to the GitHub self-hosted runners reference. Monitor queue age and capacity exhaustion, and decide how operators or users will be notified when scale-up cannot meet demand.
  • Use ephemeral runners for autoscaling. GitHub recommends ephemeral self-hosted runners for autoscaling; each handles one job and is automatically deregistered. GitHub also recommends forwarding runner logs externally before deployment so that short-lived instances do not erase the evidence needed to diagnose failures. See the runner reference.
  • Restrict what can reach the runners. Scope repository access and runner groups deliberately. GitHub warns that untrusted code—particularly from public-repository fork workflows—can compromise self-hosted runners. Its runner access documentation describes access controls and groups.
  • Test failure paths before relying on autoscaling. Exercise delayed or repeated events, unavailable client capacity, runner startup failure, cancellation, and incomplete cleanup. These are operational test cases, not behaviors guaranteed by the cited product documentation.
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A practical implementation boundary

Keep the responsibilities explicit: CI determines that work exists; an event bridge or controller requests it; Nomad dispatches and places jobs; the Autoscaler adjusts allocations or client capacity according to its configured signal; and the runner executes the CI job. If Temporal is selected to coordinate those transitions, document and verify its exact role rather than treating the name of the architecture as evidence of a supported integration.

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