License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FSTTCS.2019.24
URN: urn:nbn:de:0030-drops-115867
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/11586/
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Lucarelli, Giorgio ; Moseley, Benjamin ; Thang, Nguyen Kim ; Srivastav, Abhinav ; Trystram, Denis

Online Non-Preemptive Scheduling to Minimize Maximum Weighted Flow-Time on Related Machines

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LIPIcs-FSTTCS-2019-24.pdf (0.4 MB)

Abstract

We consider the problem of scheduling jobs to minimize the maximum weighted flow-time on a set of related machines. When jobs can be preempted this problem is well-understood; for example, there exists a constant competitive algorithm using speed augmentation. When jobs must be scheduled non-preemptively, only hardness results are known. In this paper, we present the first online guarantees for the non-preemptive variant. We present the first constant competitive algorithm for minimizing the maximum weighted flow-time on related machines by relaxing the problem and assuming that the online algorithm can reject a small fraction of the total weight of jobs. This is essentially the best result possible given the strong lower bounds on the non-preemptive problem without rejection.

BibTeX - Entry

@InProceedings{lucarelli_et_al:LIPIcs:2019:11586,
  author =	{Giorgio Lucarelli and Benjamin Moseley and Nguyen Kim Thang and Abhinav Srivastav and Denis Trystram},
  title =	{{Online Non-Preemptive Scheduling to Minimize Maximum Weighted Flow-Time on Related Machines}},
  booktitle =	{39th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2019)},
  pages =	{24:1--24:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-131-3},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{150},
  editor =	{Arkadev Chattopadhyay and Paul Gastin},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2019/11586},
  URN =		{urn:nbn:de:0030-drops-115867},
  doi =		{10.4230/LIPIcs.FSTTCS.2019.24},
  annote =	{Keywords: Online Algorithms, Scheduling, Resource Augmentation}
}

Keywords: Online Algorithms, Scheduling, Resource Augmentation
Collection: 39th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2019)
Issue Date: 2019
Date of publication: 04.12.2019

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