Towards Adaptive Resource Management for HPC Workloads in Cloud Environments
Time: Mon 2025-06-02 14.00
Location: E2, Lindstedtsvägen 3, Stockholm
Language: English
Subject area: Computer Science
Doctoral student: Daniel Araújo De Medeiros , Beräkningsvetenskap och beräkningsteknik (CST)
Opponent: Prof. Paolo Bientinesi, Umeå universitet
Supervisor: Prof. Ivy Bo Peng, Beräkningsvetenskap och beräkningsteknik (CST)
QC 20250506
Abstract
Maximizing resource efficiency is crucial when designing cloud-based systems,which are primarily built to meet specific quality-of-service requirements.Common optimization techniques include containerization, workflow orchestration,elasticity, and vertical scaling, all aimed at improving resource utilizationand reducing costs. In contrast, on-premises high-performance computingsystems prioritize maximum performance, typically relying on static resourceallocation. While this approach offers certain advantages over cloud systems,it can be restrictive in handling the increasingly dynamic resource demands oftightly coupled HPC workloads, making adaptive resource management challenging.
This thesis explores the execution of high-performance workloads in cloudbasedenvironments, investigating both horizontal and vertical scaling strategiesas well as the feasibility of running HPC workflows in the cloud. Additionally,we will evaluate the costs of deploying these workloads in containerizedenvironments and examine the advantages of using object storagein cloud-based HPC systems.