Primal-Dual and Dual-Fitting Analysis of Online Scheduling Algorithms for Generalized Flow Time Problems

  • Spyros Angelopoulos
  • Giorgio Lucarelli
  • Kim Thang Nguyen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9294)

Abstract

We study online scheduling problems on a single processor that can be viewed as extensions of the well-studied problem of minimizing total weighted flow time. In particular, we provide a framework of analysis that is derived by duality properties, does not rely on potential functions and is applicable to a variety of scheduling problems. A key ingredient in our approach is bypassing the need for “black-box” rounding of fractional solutions, which yields improved competitive ratios.

We begin with an interpretation of Highest-Density-First (HDF) as a primal-dual algorithm, and a corresponding proof that HDF is optimal for total fractional weighted flow time (and thus scalable for the integral objective). Building upon the salient ideas of the proof, we show how to apply and extend this analysis to the more general problem of minimizing ∑ jwjg(Fj), where wj is the job weight, Fj is the flow time and g is a non-decreasing cost function. Among other results, we present improved competitive ratios for the setting in which g is a concave function, and the setting of same-density jobs but general cost functions. We further apply our framework of analysis to online weighted completion time with general cost functions as well as scheduling under polyhedral constraints.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Spyros Angelopoulos
    • 1
    • 2
  • Giorgio Lucarelli
    • 3
  • Kim Thang Nguyen
    • 4
  1. 1.UPMC Univ Paris 06Sorbonne UniversitésParisFrance
  2. 2.CNRSParisFrance
  3. 3.LIG, University Grenoble-AlpesGrenobleFrance
  4. 4.IBISC, University of Evry Val d’EssonneEssonneFrance

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