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Algorithmica

, Volume 32, Issue 2, pp 163–200 | Cite as

Optimal Time-Critical Scheduling via Resource Augmentation

  • Phillips
  • Stein
  • Torng
  • Wein
Article

Abstract

We consider two fundamental problems in dynamic scheduling: scheduling to meet deadlines in a preemptive multiprocessor setting, and scheduling to provide good response time in a number of scheduling environments. When viewed from the perspective of traditional worst-case analysis, no good on-line algorithms exist for these problems, and for some variants no good off-line algorithms exist unless P = NP .

We study these problems using a relaxed notion of competitive analysis, introduced by Kalyanasundaram and Pruhs, in which the on-line algorithm is allowed more resources than the optimal off-line algorithm to which it is compared. Using this approach, we establish that several well-known on-line algorithms, that have poor performance from an absolute worst-case perspective, are optimal for the problems in question when allowed moderately more resources. For optimization of average flow time, these are the first results of any sort, for any NP -hard version of the problem, that indicate that it might be possible to design good approximation algorithms.

Key words. Scheduling, On-line algorithms, Competitive analysis, Flow time, Real-time, Extra resources. 

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Copyright information

© Springer-Verlag New York Inc. 2001

Authors and Affiliations

  • Phillips
    • 1
  • Stein
    • 2
  • Torng
    • 3
  • Wein
    • 4
  1. 1.Sandia National Laboratories, Albuquerque, NM 87183, USA. caphill@cs.sandia.gov. This work was supported in part by the United States Department of Energy under Contract DE-AC04-94AL85000.US
  2. 2.Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA. cliff@ieor.columbia.edu. Research partially supported by NSF Award CCR-9308701 and NSF Career Award CCR-9624828. This work was done while this author was at Dartmouth College. Some of this work was done while this author was visiting Stanford University, and while visiting the first author at Sandia National Laboratories.US
  3. 3.Department of Computer Science and Engineering, 3115 Engineering Building, Michigan State University, East Lansing, MI 48824, USA. torng@cse.msu.edu. Research partially supported by NSF CAREER Grant CCR-9701679, a grant from IBM, and an MSU research initiation grant. Some preliminary work was done while this author was a Stanford graduate student, supported by a DOD NDSEG Fellowship, NSF Grant CCR-9010517, Mitsubishi Corporation, and NSF YI Award CCR-9357849, with matching funds from IBM, Schlumberger Foundation, Shell Foundation, and Xerox Corporation.US
  4. 4.Department of Computer Science, Polytechnic University, Brooklyn, NY 11201, USA. wein@mem.poly.edu. Research partially supported by NSF Research Initiation Award CCR-9211494, NSF Grant CCR-9626831, and a grant from the New York State Science and Technology Foundation, through its Center for Advanced Technology in Telecommunications.US

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