Scheduling parallelizable jobs online to maximize throughput

Kunal Agrawal, Jing Li, Kefu Lu, Benjamin Moseley

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

4 Scopus citations

Abstract

In this paper, we consider scheduling parallelizable jobs online to maximize the throughput or profit of the schedule. In particular, a set of n jobs arrive online and each job Ji arriving at time ri has an associated function pi(t) which is the profit obtained for finishing job Ji at time t+ ri. Each job can have its own arbitrary non-increasing profit function. We consider the case where each job is a parallel job that can be represented as a directed acyclic graph (DAG). We give the first non-trivial results for the profit scheduling problem for DAG jobs and show O(1)-competitive algorithms using resource augmentation.

Original languageEnglish (US)
Title of host publicationLATIN 2018
Subtitle of host publicationTheoretical Informatics - 13th Latin American Symposium, Proceedings
EditorsMiguel A. Mosteiro, Michael A. Bender, Martin Farach-Colton
PublisherSpringer Verlag
Pages755-776
Number of pages22
ISBN (Print)9783319774039
DOIs
StatePublished - Jan 1 2018
Event13th International Symposium on Latin American Theoretical Informatics, LATIN 2018 - Buenos Aires, Argentina
Duration: Apr 16 2018Apr 19 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10807 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Symposium on Latin American Theoretical Informatics, LATIN 2018
Country/TerritoryArgentina
CityBuenos Aires
Period4/16/184/19/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Scheduling parallelizable jobs online to maximize throughput'. Together they form a unique fingerprint.

Cite this