Non-clairvoyant Weighted Flow Time Scheduling on Different Multi-processor Models

  • Jianqiao Zhu
  • Ho-Leung Chan
  • Tak-Wah Lam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7164)

Abstract

We study non-clairvoyant scheduling to minimize weighted flow time on two different multi-processor models. In the first model, processors are all identical and jobs can possibly be speeded up by running on several processors in parallel. Under the non-clairvoyant model, the online scheduler has no information about the actual job size and degree of speed-up due to parallelism during the execution of a job, yet it has to determine dynamically when and how many processors to run the jobs. The literature contains several O(1)-competitive algorithms for this problem under the unit-weight multi-processor setting [9,10] as well as the weighted single-processor setting [2]. This paper shows the first O(1)-competitive algorithm for weighted flow time in the multi-processor setting.

In the second model, we consider processors with different functionalities and only processors of the same functionality can work on the same job in parallel to achieve some degree of speed up. Here a job is modeled as a sequence of non-clairvoyant demands of different functionalities. This model is derived naturally from the classical job shop scheduling; but as far as we know, there is no previous work on scheduling to minimize flow time under this multi-processor model. In this paper we take a first step to study non-clairvoyant scheduling on this multi-processor model. Motivated by the literature on 2-machine job shop scheduling, we focus on the special case when processors are divided into two types of functionalities, and we show a non-clairvoyant algorithm that is O(1)-competitive for weighted flow time.

Keywords

Online Algorithm Competitive Algorithm Heterogeneous Processor Weighted Flow Time Online Scheduler 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jianqiao Zhu
    • 1
  • Ho-Leung Chan
    • 1
  • Tak-Wah Lam
    • 1
  1. 1.University of Hong KongHong Kong

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