15 posts tagged with "volcano"

Volcano v1.16.0 introduces NamespaceQueue and the fairshare plugin. Tenants can use hierarchical queues within their own namespaces to organize quotas and queue hierarchies within administrator-authorized resource pools. Fairshare brings historical resource consumption into scheduling decisions among namespaces sharing a queue, improving fairness across teams.

This release also expands the HyperNode ecosystem. Platforms using other schedulers can deploy the HyperNode controller on its own to discover topology and manage supernodes and their hierarchy, then integrate that information through the HyperNode API. For inference services using Volcano, new PodGroup topology anti-affinity rules can spread instances across supernodes to reduce exposure to a shared failure domain.

Scheduler performance improvements include an Unschedulable Job Cache that records why Jobs could not be scheduled and skips repeated checks until blocking conditions change. Node scoring, cache updates, and queue lookups also become more efficient. The release additionally brings Kubernetes 1.36 support, load estimation for bulk Pod dispatch, DRA scoring, and accelerator and stability improvements.

As batch training, inference, AI Agent, HPC, big-data and other diverse workloads are increasingly co-located in the same Kubernetes cluster, the scheduler must make higher-quality decisions under intensifying resource contention while preserving job-level semantics, queue fairness, topology affinity, and operational stability. v1.15.0 delivers enhancements across the scheduling core, heterogeneous resource management, multi-scheduler coordination, and performance observability.

The most notable new capability is Gang-Aware Preemption and Resource Reclamation: preemption decisions are evaluated at gang granularity on both the preemptor and victim sides — the preemptor is placed as a whole gang, and victim candidates are organized and evaluated at job/gang granularity, preferring surplus replicas to avoid per-Pod random eviction that disrupts multiple training jobs while the preemptor itself still cannot start. In addition, v1.15.0 introduces DRA queue quota in the capacity plugin, a pluggable multi-sharding policy framework, a Benchmark and performance observability tool, Kubernetes 1.35 support, NodeGroup preferred ordering, Agent Scheduler stability fixes, GPU/vGPU incremental enhancements, and Scheduling Gates for queue admission control.

Volcano community v1.14 is now officially released. As AI workloads evolve from single offline training to diverse scenarios including online inference and AI Agents, the scheduling system faces unprecedented challenges. v1.14 delivers architecture-level innovations that maintain Volcano's advantages in large-scale batch computing while closing the gap for latency-sensitive workloads, taking a solid step toward the goal of becoming a "unified scheduling platform for AI training, inference, RL, and Agent scenarios."

On September 29, 2025 (Beijing Time), [Volcano v1.13] (https://github.com/volcano-sh/volcano/releases/tag/v1.13.0)[1] was officially released. This update brings functional enhancements across multiple areas, providing users with a more comprehensive cloud-native batch computing solution.

Volcano v1.12 released: Advancing Cloud-Native AI and Batch Computing​

As AI large model technology rapidly evolves, enterprises are placing higher demands on computing resource efficiency and application performance. For complex application scenarios such as AI, big data, and high-performance computing (HPC), efficiently utilizing accelerators like GPUs, ensuring high system availability, and managing resources with fine granularity are the core areas of focus for the Volcano community's continuous innovation.

Volcano is excited to announce the completion of our CNCF-funded security audit carried out by Ada Logics and facilitated by OSTIF in collaboration with the Volcano maintainers. The audit was scoped to cover the Volcano source code, supply-chain risks and fuzzing. The auditing team identified 10 security issues which the Volcano security team has fixed with the completion of the audit.

As the de facto standard in cloud-native batch computing, Volcano has been widely adopted across various scenarios, including AI, Big Data, and High-Performance Computing (HPC). With over 800 contributors from more than 30 countries and tens of thousands of code commits, Volcano has been deployed in production environments by over 60 enterprises worldwide. It provides the industry with excellent practical standards and solutions for cloud native batch computing.

On Sep 19, 2024, UTC+8, Volcano version v1.10.0 was officially released. This version introduced the following new features:

On May 21, 2024, UTC+8, Volcano version v1.9.0 was officially released. This version added the following new features:

Cloud native batch computing engine Volcano is designed for high-performance computing applications such as AI, big data, gene sequencing, and rendering, and supports mainstream general computing frameworks. More than 58,000 global developers joined us, among whom the in-house ones come from companies such as Huawei, AWS, Baidu, Tencent, JD, and Xiaohongshu. There are 3.7k+ Stars and 800+ Forks for the project. Volcano has been proven feasible for mass data computing and analytics, such as AI, big data, and gene sequencing. Supported frameworks include Spark, Flink, TensorFlow, PyTorch, Argo, MindSpore, Paddlepaddle, Kubeflow, MPI, Horovod, MXNet, KubeGene, and Ray. The ecosystem is thriving with more developers and use cases coming up.