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While the default kube-scheduler is incredibly robust, highly specialized workloads (like high-performance computing, machine learning training, or massive batch processing) require scheduling algorithms that the default scheduler cannot easily provide.
To solve this, Kubernetes allows you to run multiple schedulers simultaneously and specify which scheduler should handle which Pods.
Volcano is a batch scheduling system built on Kubernetes. It provides a suite of mechanisms that are commonly required by high-performance workloads but are missing in the default scheduler.
Machine learning jobs (like TensorFlow distributed training) require multiple Pods (workers) to run simultaneously. If the cluster only has enough room to schedule half of the workers, the default scheduler will start them anyway, where they will sit idle waiting for the other half, wasting expensive GPU resources.
Volcano implements Gang Scheduling. It evaluates the entire job as an "all-or-nothing" group. It will only schedule the Pods if there are enough resources in the cluster to run all of them simultaneously.
The default scheduler only makes decisions when a Pod is first created. Once a Pod is running on a Node, it stays there until it dies, even if the cluster state changes dramatically. Over time, this leads to fragmented and unbalanced clusters.
The Descheduler is an optional tool that runs periodically. It scans the cluster and actively evicts running Pods that violate policies (like PodAntiAffinity that was added after the Pod was scheduled) or simply to rebalance heavily utilized nodes. The evicted Pods are then rescheduled by the default scheduler onto better nodes.
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