Select and permute: An improved online framework for scheduling to minimize weighted completion time

Samir Khuller, Jingling Li, Pascal Sturmfels, Kevin Sun, Prayaag Venkat*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this paper, we introduce a new online scheduling framework for minimizing total weighted completion time in a general setting. The framework is inspired by the work of Hall et al. (1997) [11] and Garg et al. (2007) [9], who show how to convert an offline approximation to an online scheme. Our framework uses two offline approximation algorithms—one for the simpler problem of scheduling without release times, and another for the minimum unscheduled weight problem—to create an online algorithm with provably good competitive ratios. We illustrate multiple applications of this method that yield improved competitive ratios. Our framework gives algorithms with the best or only-known competitive ratios for the concurrent open shop, coflow, and concurrent cluster models. We also introduce a randomized variant of our framework based on the ideas of Chakrabarti et al. (1996) [3] and use it to achieve improved competitive ratios for these same problems.

Original languageEnglish (US)
Pages (from-to)420-431
Number of pages12
JournalTheoretical Computer Science
Volume795
DOIs
StatePublished - Nov 26 2019

Keywords

  • Coflow scheduling
  • Concurrent clusters
  • Concurrent open shop
  • Online algorithms

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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