multi-resource-allocation-validation

1# Multi-Resource Allocation Validation
2 
3Use this skill before returning a batch of resource allocation actions, and after building a feasible schedule to make small objective improvements.
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5## Core Workflow
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7Replay every proposed action against a temporary resource state. A placement is valid only if each required resource remains non-negative after applying all earlier placements in the same batch. Do not validate each placement only against the original observation.
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9Example field names vary by task, but common reminders include `cpu_free`, `memory_free`, and `gpu_slots[*].free_gpu_units`.
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11## Replay Skeleton
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13Use replay validation as the final gate before returning actions:
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15```text
16temporary_state = copy_resources(original_observation)
17repaired_actions = []
18 
19for action in actions:
20  if action is not a placement:
21    repaired_actions.append(action)
22    continue
23 
24  find the work item, target machine, and target slot/resource
25  check compatibility
26  check every required resource is available
27 
28  if any check fails:
29    action = repair_or_replace_with_defer_or_reject(action, temporary_state)
30 
31  if action is still a placement:
32    subtract consumed resources from temporary_state
33 
34  repaired_actions.append(action)
35```
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37The combined action list must be feasible after all earlier actions in the same batch have consumed resources. Each work item should appear in at most one action, and deferred or rejected work should not consume resources.
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39## Repair Order
40…