Volcano v1.15.0 introduces critical enhancements for AI and batch workloads, most notably gang-aware preemption to protect workload integrity during resource contention. It expands resource management with DRA quota support and scheduling gates to prevent unnecessary autoscaling. The release also adds a robust benchmarking framework and pluggable sharding policies, while ensuring compatibility with Kubernetes 1.35.

Gang-Aware Preemption and Resource Reclamation (Alpha) — This prevents the "partial eviction" problem where a single training task is killed, rendering the rest of the gang useless while still consuming resources. It ensures that when resources are reclaimed, they are taken in logical job units, preserving the integrity of AI/ML training workloads.

DRA Queue Quota in Capacity Plugin — AI engineers using Dynamic Resource Allocation (DRA) can now enforce hardware quotas (like specific GPU types or vGPU partitions) within Volcano queues, preventing one team from consuming all specialized accelerator resources.

Scheduling Gates for Queue Admission Control (Alpha) — Cluster operators can now use 'Scheduling Gates' to stop queue-blocked pods from triggering Cluster Autoscaler or Karpenter. This prevents expensive and unnecessary cloud infrastructure scale-ups when the bottleneck is a soft software limit (quota) rather than hard physical capacity.

New Benchmark Framework and Observability Tooling — Provides a standardized way to measure scheduling throughput and latency on your specific hardware. This allows data platform teams to identify bottlenecks and optimize Volcano for large-scale, high-concurrency job submission.

Pluggable Multi-Sharding Policies with Live Reload — Allows more granular control over multi-scheduler deployments. Operators can apply custom policies (like warmup or allocation rates) to specific shards via ConfigMap without restarting the controller, improving operational flexibility.