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Approximation schemes for preemptive weighted flow time
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Source Annual ACM Symposium on Theory of Computing archive
Proceedings of the thiry-fourth annual ACM symposium on Theory of computing table of contents
Montreal, Quebec, Canada
SESSION: Session 6A table of contents
Pages: 297 - 305  
Year of Publication: 2002
ISBN:1-58113-495-9
Authors
Chandra Chekuri  Bell Labs, Murray Hill, NJ
Sanjeev Khanna  University of Pennsylvania, Philadelphia, PA
Sponsor
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 7,   Downloads (12 Months): 37,   Citation Count: 12
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ABSTRACT

(MATH) We present the first approximation schemes for minimizing weighted flow time on a single machine with preemption. Our first result is an algorithm that computes a (1+&egr;)-approximate solution for any instance of weighted flow time in O(nO(ln W ln P/&egr;3)) time; here P is the ratio of maximum job processing time to minimum job processing time, and W is the ratio of maximum job weight to minimum job weight. This result directly gives a quasi-PTAS for weighted flow time when P and W are poly-bounded, and a PTAS when they are both O(1). We strengthen the former result to show that in order to get a quasi- PTAS it suffices to have just one of P and W to be poly-bounded. Our result provides strong evidence to the hypothesis that the weighted flow time problem has a PTAS. We note that the problem is strongly NP-hard even when P and W are O(1). We next consider two important special cases of weighted flow time, namely, when P is O(1) and W is arbitrary, and when the weight of a job is inverse of its processing time referred to as the stretch metric. For both of the above special cases we obtain a (1+&egr;)-approximation for any &egr; ρ 0 by using a randomized partitioning scheme to reduce an arbitrary instance to several instances all of which have P and W bounded by a constant that depends only on &egr;.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  12

Collaborative Colleagues:
Chandra Chekuri: colleagues
Sanjeev Khanna: colleagues