Scheduling independent stochastic tasks under deadline and budget constraints. (March 2020)

• Record Type: Journal Article
• Authors: Canon, Louis-Claude; Chang, Aurélie Kong Win; Robert, Yves; Vivien, Frédéric
• Other Names: Wang Jue guest-editor.; He XinFu guest-editor.
• Is Part Of: International journal of high performance computing applications. Volume 34:Number 2(2020)
• Page Start: 246
• Page End: 264
• Publication Date: 2020-03
• Subjects: Independent tasks -- stochastic cost -- scheduling -- budget -- deadline -- cloud platform; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342019852135
• Languages: English
• ISSNs: 1094-3420

Abstract

This article discusses scheduling strategies for the problem of maximizing the expected number of tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The execution times of tasks follow independent and identically distributed probability laws. The main questions are how many processors to enroll and whether and when to interrupt tasks that have been executing for some time. We provide complexity results and an asymptotically optimal strategy for the problem instance with discrete probability distributions and without deadline. We extend the latter strategy for the general case with continuous distributions and a deadline and we design an efficient heuristic which is shown to outperform standard approaches when running simulations for a variety of useful distribution laws.