| Minimizing weighted flow time |
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Symposium on Discrete Algorithms
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Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
table of contents
Baltimore, Maryland
SESSION: Session 8A
table of contents
Pages: 508 - 516
Year of Publication: 2003
ISBN:0-89871-538-5
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Authors
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N. Bansal
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Carnegie Mellon University, Pittsburgh, PA
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K. Dhamdhere
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Carnegie Mellon University, Pittsburgh, PA
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Society for Industrial and Applied Mathematics
Philadelphia, PA, USA
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| Bibliometrics |
Downloads (6 Weeks): 3, Downloads (12 Months): 20, Citation Count: 5
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ABSTRACT
We consider the problem of minimizing weighted flow time on a single machine in the preemptive setting. Our main result is an O(log W) competitive online algorithm where the maximum to the minimum ratio of weights is W. More generally our algorithm achieves a competitive ratio of k if there are k weight classes. This gives the first O(1)-competitive algorithm for constant k. No O(1) competitive algorithm was known previously even for the special case of k = 2. These results settle a question posed by Chekuri et al [5] about the existence of a "truly" online algorithm with a non-trivial competitive ratio. We also give a "semi-online" algorithm with competitive ratio O(log n + log P), where P is ratio of the maximum to minimum job size. Our second result deals with the non-clairvoyant setting where the job sizes are unknown (but the weight of the jobs are known). We consider the resource augmentation model, and give a non-clairvoyant online algorithm, which if allowed a (1 + ε) speed-up, is (1 + l/ε) competitive against an optimal offline, clairvoyant algorithm.
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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Foto Afrati , Evripidis Bampis , Chandra Chekuri , David Karger , Claire Kenyon , Sanjeev Khanna , Ioannis Milis , Maurice Queyranne , Martin Skutella , Cliff Stein , Maxim Sviridenko, Approximation Schemes for Minimizing Average Weighted Completion Time with Release Dates, Proceedings of the 40th Annual Symposium on Foundations of Computer Science, p.32, October 17-18, 1999
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CITED BY 5
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Chandra Chekuri , Ashish Goel , Sanjeev Khanna , Amit Kumar, Multi-processor scheduling to minimize flow time with ε resource augmentation, Proceedings of the thirty-sixth annual ACM symposium on Theory of computing, June 13-16, 2004, Chicago, IL, USA
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