Online-bounded analysis

Joan Boyar, Leah Epstein, Lene M. Favrholdt, Kim S. Larsen, Asaf Levin

Research output: Contribution to journalArticlepeer-review

Abstract

Though competitive analysis is often a very good tool for the analysis of online algorithms, sometimes it does not give any insight and sometimes it gives counter-intuitive results. Much work has gone into exploring other performance measures, in particular targeted at what seems to be the core problem with competitive analysis: The comparison of the performance of an online algorithm is made with respect to a too powerful adversary. We consider a new approach to restricting the power of the adversary, by requiring that when judging a given online algorithm, the optimal offline algorithm must perform at least as well as the online algorithm, not just on the entire final request sequence, but also on any prefix of that sequence. This is limiting the adversary’s usual advantage of being able to exploit that it knows the sequence is continuing beyond the current request. Through a collection of online problems, including machine scheduling, bin packing, dual bin packing, and seat reservation, we investigate the significance of this particular offline advantage.

Original languageEnglish
Pages (from-to)429-441
Number of pages13
JournalJournal of Scheduling
Volume21
Issue number4
DOIs
StatePublished - 1 Aug 2018

Bibliographical note

Funding Information:
Acknowledgements Funding was provided by The Danish Council for Independent Research (Grant No. DFF-1323-00247) and The Villum Foundation (Grant No. VKR023219).

Funding Information:
Supported in part by the Danish Council for Independent Research, Natural Sciences, and the Villum Foundation. A preliminary version of this paper appeared in the Eleventh International Computer Science Symposium in Russia, Lecture Notes in Computer Science, vol. 9691, Springer, 2016, pp. 131–145.

Publisher Copyright:
© 2017, Springer Science+Business Media, LLC.

Keywords

  • Bin packing
  • Machine scheduling
  • Online algorithms
  • Quality measures

ASJC Scopus subject areas

  • Software
  • Engineering (all)
  • Management Science and Operations Research
  • Artificial Intelligence

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