Volcano
v1.13.2Orchestration & ManagementVolcano v1.13.2 is a focused patch release fixing five bugs: a panic in NUMA resource handling, GPU resource errors, scheduler snapshot corruption, and incorrect terminating pod behavior in jobs.
breakingScheduler snapshot mutation bug can corrupt scheduling decisions — upgrade now
The shared mutable objects bug in scheduler snapshot clones (PR #5093) is the most serious fix here. If multiple scheduling cycles inadvertently share state, you get non-deterministic scheduling behavior that's extremely hard to diagnose. Any cluster running GPU or multi-task batch jobs under load should treat this as urgent. Upgrade from v1.13.1 to v1.13.2 immediately.
securityPanic in NUMA snapshot could crash the scheduler — patch before scaling NUMA workloads
A nil pointer / concurrent-write panic in NUMA resource info updating during snapshots can bring down the volcano-scheduler process. On NUMA-aware clusters, this means scheduling halts entirely until the pod restarts. If you're running topology-aware workloads, don't wait — patch to v1.13.2 before expanding those workloads.
enhancementGPU resource fix prevents ghost allocations on GPU nodes
The GPU resource error fix addresses incorrect resource accounting that could lead to nodes appearing over-allocated or under-utilized. If you've noticed GPU pods stuck in Pending despite apparent capacity, or unexpected scheduling failures on GPU nodes, this patch likely resolves it. After upgrading, force a reconciliation by restarting the scheduler pod to clear any stale resource state.
主な変更 (5)
- Terminating pods now correctly stay within their job scope instead of being dropped prematurely
- Fixed a potential panic when updating NUMA resource info during scheduler snapshot operations
- GPU resource accounting errors corrected — miscounts could cause over- or under-scheduling of GPU workloads
- Prometheus metrics client updated to fix reporting issues
- Scheduler snapshot clones no longer share mutable objects, preventing subtle state corruption across scheduling cycles