Portfolio scheduling for managing operational and disaster-recovery risks in virtualized datacenters hosting business-critical workloads

Vincent Van Beek, Giorgos Oikonomou, Alexandru Iosup

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

Abstract

Cloud datacenters are increasingly hosting business workloads. Such long-running, on-demand workloads raise important challenges in datacenter operation, requiring efficient online scheduling of workloads with unprecedented characteristics under strict service level agreements (SLAs). In this work, we propose an approach to manage the risk of not meeting SLAs. Our approach is based on portfolio scheduling, which is an online scheduling technique that dynamically selects a scheduling algorithm from a set (portfolio), subject to a possibly changing utility function. Ours is the first datacenter-scheduling approach to consider operational and disaster-recovery risks. Using trace-based simulation with traces collected from a commercial multi-datacenter environment, we give evidence that portfolio scheduling is able to mitigate risks significantly better than its constituent scheduling algorithms and better than datacenter engineers.

Original languageEnglish
Title of host publicationProceedings - 2019 18th International Symposium on Parallel and Distributed Computing, ISPDC 2019
EditorsAlexandru Iosup, Florin Pop, Radu Prodan, Alexandru Uta
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages94-102
Number of pages9
ISBN (Electronic)9781728138008
DOIs
Publication statusPublished - 3 Jun 2019
Event18th International Symposium on Parallel and Distributed Computing, ISPDC 2019 - Amsterdam, Netherlands
Duration: 5 Jun 20197 Jun 2019

Conference

Conference18th International Symposium on Parallel and Distributed Computing, ISPDC 2019
CountryNetherlands
CityAmsterdam
Period5/06/197/06/19

Keywords

  • Portfolio Scheduling, Datacenter Resource Management, Risk Management, Risk Tolerance, Operational Risk, Disaster Recoverability Risk

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