Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS clouds

Kefeng Deng, Junqiang Song, Kaijun Ren, Alexandru Iosup

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Long-term execution of scientific applications often leads to dynamic workloads and varying application requirements. When the execution uses resources provisioned from IaaS clouds, and thus consumption-related payment, efficient and online scheduling algorithms must be found. Portfolio scheduling, which selects dynamically a suitable policy from a broad portfolio, may provide a solution to this problem. However, selecting online the right policy from possibly tens of alternatives remains challenging. In this work, we introduce an abstract model to explore this selection problem. Based on the model, we present a comprehensive portfolio scheduler that includes tens of provisioning and allocation policies. We propose an algorithm that can enlarge the chance of selecting the best policy in limited time, possibly online. Through trace-based simulation, we evaluate various aspects of our portfolio scheduler, and find performance improvements from 7% to 100% in comparison with the best constituent policies and high improvement for bursty workloads. Copyright 2013 ACM.
Original languageEnglish
Title of host publicationProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC'13, Denver, CO, USA - November 17 - 21, 2013
PublisherACM, IEEE Computer Society
Pages55:1-55:12
ISBN (Print)9781450323789
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2013 - Denver, United States
Duration: 17 Nov 201322 Nov 2013

Conference

Conference2013 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2013
CountryUnited States
CityDenver
Period17/11/1322/11/13

Keywords

  • IaaS Cloud
  • Portfolio Scheduling
  • Resource Provisioning
  • Scientific Workloads

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