Minimising the Execution of Unknown Bag-of-Task Jobs with Deadlines on the Cloud

Long Thai, Blesson Varghese, Adam Barker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Scheduling jobs with deadlines, each of which defines the latest time that a job must be completed, can be challenging on the cloud due to incurred costs and unpredictable performance. This problem is further complicated when there is not enough information to effectively schedule a job such that its deadline is satisfied, and the cost is minimised. In this paper, we present an approach to schedule jobs, whose performance are unknown before execution, with deadlines on the cloud. By performing a sampling phase to collect the necessary information about those jobs, our approach delivers the scheduling decision within 10% cost and 16% violation rate when compared to the ideal setting, which has complete knowledge about each of the jobs from the beginning. It is noted that our proposed algorithm outperforms existing approaches, which use a fixed amount of resources by reducing the violation cost by at least two times.
Original languageEnglish
Title of host publicationDIDC '16 Proceedings of the ACM International Workshop on Data-Intensive Distributed Computing, in conjunction with the 25th International ACM Symposium on High Performance Parallel and Distributed Computing (HPDC)
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Print)978-1-4503-2138-9
DOIs
Publication statusPublished - Jun 2016
Event25th International Symposium on High-Performance Parallel and Distributed Computing - Kyoto, Japan
Duration: 31 May 201604 Jun 2016
http://www.hpdc.org/2016/

Conference

Conference25th International Symposium on High-Performance Parallel and Distributed Computing
Country/TerritoryJapan
CityKyoto
Period31/05/201604/06/2016
Internet address

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