Which strategy is most effective in balancing GPU workload across a Kubernetes cluster with underutilized and overloaded nodes?

Deploying a GPU-aware scheduler in Kubernetes is the most effective strategy for balancing GPU workloads across a cluster with nodes that may be underutilized or overloaded. A GPU-aware scheduler is specifically designed to understand the resource requirements of GPU workloads, ensuring that pods scheduled for execution are allocated to the most appropriate nodes based on GPU availability and utilization.

By making scheduling decisions that take GPU resources into account, the scheduler can distribute workloads more evenly across nodes. This helps to prevent scenarios where some nodes have too many workloads (overloaded) while others have insufficient workloads (underutilized). The intelligent scheduling ensures higher resource efficiency and can lead to better overall performance of applications relying on GPU resources.

In contrast, reducing the number of GPU nodes could exacerbate the problem of resource imbalance, as it limits the available physical resources for workloads. Implementing GPU resource quotas may help control individual pod usage, but it does not effectively address the overall distribution of workloads across the cluster. Using CPU-based autoscaling also does not specifically target GPU resources and may not resolve issues related to GPU workload balancing, as it focuses on CPU scaling rather than GPU need.