Scheduling shared continuous resources on many-cores
journal contributionposted on 2018-02-06, 11:06 authored by Ernst Althaus, Andre Brinkmann, Peter Kling, Friedhelm Meyer Auf der Heide, Lars NagelLars Nagel, Soren Riechers, Jiri Sgall, Tim Suss
© 2017 Springer Science+Business Media New York We consider the problem of scheduling a number of jobs on m identical processors sharing a continuously divisible resource. Each job j comes with a resource requirement [InlineEquation not available: see fulltext.]. The job can be processed at full speed if granted its full resource requirement. If receiving only an x-portion of (Formula presented.), it is processed at an x-fraction of the full speed. Our goal is to find a resource assignment that minimizes the makespan (i.e., the latest completion time). Variants of such problems, relating the resource assignment of jobs to their processing speeds, have been studied under the term discrete–continuous scheduling. Known results are either very pessimistic or heuristic in nature. In this article, we suggest and analyze a slightly simplified model. It focuses on the assignment of shared continuous resources to the processors. The job assignment to processors and the ordering of the jobs have already been fixed. It is shown that, even for unit size jobs, finding an optimal solution is NP-hard if the number of processors is part of the input. Positive results for unit size jobs include a polynomial-time algorithm for any constant number of processors. Since the running time is infeasible for practical purposes, we also provide more efficient algorithm variants: an optimal algorithm for two processors and a [InlineEquation not available: see fulltext.] -approximation algorithm for m processors.
Supported by the Federal Ministry of Education and Research (BMBF) under Grant 01IH13004 (Project “FAST”), by the German Research Foundation (DFG) within the Collaborative Research Center “On-The-Fly Computing” (SFB 901), by the Center of Excel- lence – ITI (project P202/12/G061 of GA CR), and by the Pacific Institute of Mathematical Sciences (PIMS).
- Computer Science