Towards a Novel Vertical Scaling Approach for Bursty Workloads in Kubernetes

Traditional static computational resource allocation in cloud or on-premises clusters often results in inefficient overprovisioning. Users frequently lack precise knowledge of the memory and processors their applications require, leading them to request excess resources. This causes wasted capacity, higher costs, and, in shared environments, longer queue waiting times. Dynamic resource allocation through autoscaling addresses this issue by adjusting resources at runtime. Kubernetes, a widely used container orchestration platform, supports autoscaling via Horizontal and Vertical Pod Autoscalers. However, its default restart-based scaling can disrupt stateful, long-running workloads without checkpointing. This work leverages Kubernetes’ new in-place scaling, which resizes resources without restarts, to propose the Dynamic Resizing Strategy (DRS), a novel autoscaling approach that proactively manages contention by temporarily throttling co-located pods to prioritize a bursting application. We evaluate it with NAS Parallel Benchmarks and synthetic workloads in co-execution scenarios, showing improved efficiency and stability, increasing success rates and reducing global average wait time by over 18% compared to the Burstable QoS class.