Volcano v1.15.1

This patch release focuses on hardening the reliability of batch scheduling, specifically addressing race conditions in PVC management and improving the accuracy of resource accounting for AI hardware like Ascend vNPUs and HAMi vGPUs. Significant performance tweaks to the scheduler's filtering and scoring logic also aim to provide better responsiveness in large-scale deployments.

Resolved PVC informer race condition causing stuck workloads — Prevents a critical race condition where workloads using OnDemand PVCs could get stuck in a 'Pending' state indefinitely, ensuring reliable data persistence for long-running AI/ML jobs.

Improved Ascend vNPU and HAMi vGPU hardware support — Ensures compatibility with both new and legacy HAMi Ascend vNPU configurations and improves resource accounting for vGPUs and vNPUs, which is critical for AI engineers utilizing specialized hardware.

Scheduler efficiency optimizations via BatchNodeOrderFn and Filter skipping — By unifying Score plugins and allowing predicate filters to skip unnecessary operations, the scheduler can process pods faster, reducing latency in large-scale cluster environments.

Corrected scalar resource accounting in scheduling queues — Fixes an accounting error where scalar resources in the queue were not using milli-units, preventing incorrect scheduling decisions and potential resource starvation.

Dependency update for crypto and SSH security fixes — Mitigates potential SSH-related vulnerabilities by updating core cryptographic dependencies, ensuring the security of the control plane.