Online Non-preemptive Scheduling on Unrelated Machines with Rejections
Lucarelli, Giorgio; Moseley, Benjamin; Thang, Nguyen Kim; Srivastav, Abhinav; Trystram, Denis (2018), Online Non-preemptive Scheduling on Unrelated Machines with Rejections, SPAA '18: Proceedings of the 30th ACM Symposium on Parallelism in Algorithms and Architectures, ACM - Association for Computing Machinery : New York, NY, p. 291-300. 10.1145/3210377.3210402
Type
Communication / ConférenceExternal document link
https://arxiv.org/abs/1802.10309v1Date
2018Conference title
SPAA 2018 - 30th ACM Symposium on Parallelism in Algorithms and ArchitecturesConference date
2018-07Conference city
ViennaConference country
AustriaBook title
SPAA '18: Proceedings of the 30th ACM Symposium on Parallelism in Algorithms and ArchitecturesPublisher
ACM - Association for Computing Machinery
Published in
New York, NY
ISBN
978-1-4503-5799-9
Pages
291-300
Publication identifier
Metadata
Show full item recordAuthor(s)
Lucarelli, Giorgio
Moseley, Benjamin
Thang, Nguyen Kim
Srivastav, Abhinav
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Trystram, Denis
Abstract (EN)
When a computer system schedules jobs there is typically a significant cost associated with preempting a job during execution. This cost can be from the expensive task of saving the memory's state and loading data into and out of memory. There is a need for non-preemptive system schedulers to avoid the costs of preemption on desktops, servers and data centers. Despite this need, there is a gap between theory and practice. Indeed, few non-preemptive online schedulers are known to have strong foundational guarantees. This gap is likely due to strong lower bounds on any online algorithm for popular objectives. Indeed, typical worst case analysis approaches, and even resource augmented approaches such as speed augmentation, result in all algorithms having poor performance guarantees. This paper considers online non-preemptive scheduling problems in the worst-case model where the algorithm is allowed to reject a small fraction of jobs. By rejecting only few jobs, this paper shows that the strong lower bounds can be circumvented. This model can be used to discover scheduling policies with desirable worst-case guarantees. Specifically, the paper presents algorithms for minimizing the total flow-time and minimizing the total weighted flow-time plus energy under the speed-scaling mechanism. The algorithms have a small constant competitive ratio while rejecting only a constant fraction of jobs. Beyond specific results, the paper asserts that alternative models beyond speed augmentation should be explored to aid in the discovery of good schedulers in the face of the requirement of being online and non-preemptive.Subjects / Keywords
Theory of computation; Design and analysis of algorithms; Approximation algorithms analysis; Scheduling algorithms; Online algorithmsRelated items
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