ARC-V: Vertical Resource Adaptivity for HPC Workloads in Containerized Environments

TL;DR

This paper introduces ARC-V, a novel vertical autoscaling policy tailored for HPC workloads in containerized environments, addressing inefficiencies of existing autoscalers by leveraging memory consumption patterns for elastic resource provisioning.

Contribution

The paper presents ARC-V, a new autoscaling approach that improves memory efficiency and reduces errors for HPC applications in Kubernetes, unlike traditional cloud-focused autoscalers.

Findings

ARC-V reduces memory usage compared to standard VPA.

ARC-V prevents out-of-memory errors in HPC workloads.

ARC-V effectively adapts to HPC memory consumption patterns.

Abstract

Existing state-of-the-art vertical autoscalers for containerized environments are traditionally built for cloud applications, which might behave differently than HPC workloads with their dynamic resource consumption. In these environments, autoscalers may create an inefficient resource allocation. This work analyzes nine representative HPC applications with different memory consumption patterns. Our results identify the limitations and inefficiencies of the Kubernetes Vertical Pod Autoscaler (VPA) for enabling memory elastic execution of HPC applications. We propose, implement, and evaluate ARC-V. This policy leverages both in-flight resource updates of pods in Kubernetes and the knowledge of memory consumption patterns of HPC applications for achieving elastic memory resource provisioning at the node level. Our results show that ARC-V can effectively save memory while eliminating out-of-memory errors compared to the standard Kubernetes VPA.