dc.contributor.author Krishnan, P. dc.contributor.author Long, Philip M. dc.contributor.author Vitter, Jeffrey Scott dc.date.accessioned 2011-03-21T16:35:19Z dc.date.available 2011-03-21T16:35:19Z dc.date.issued 1999-01 dc.identifier.citation P. Krishnan, P. M. Long, and J. S. Vitter. “Adaptive Disk Spindown via Optimal Rent-to-Buy in Probabilistic Environments,” Algorithmica, 23(1), January 1999, 31–56. An extended abstract appears in “Learning to Make Rent-to-Buy Decisions in Probabilistic Environments with Systems Applications,” Proceedings of the 12th International Conference on Machine Learning (ML ’95), Tahoe City, CA, July 1995, 322–330. http://dx.doi.org/10.1007/PL00009249 dc.identifier.uri http://hdl.handle.net/1808/7198 dc.description The original publication is available at www.springerlink.com dc.description.abstract In the single rent-to-buy decision problem, without a priori knowledge of the amount of time a resource will be used we need to decide when to buy the resource, given that we can rent the resource for $1 per unit time or buy it once and for all for$c. In this paper we study algorithms that make a sequence of single rent-to-buy decisions, using the assumption that the resource use times are independently drawn from an unknown probability distribution. Our study of this rent- to-buy problem is motivated by important systems applications, speci cally, problems arising from deciding when to spindown disks to conserve energy in mobile computers [DKM, LKH, MDK], thread blocking decisions during lock acquisition in multiprocessor applications [KLM], and virtual circuit holding times in IP-over-ATM networks [KLP, SaK]. We develop a provably optimal and computationally e cient algorithm for the rent-to-buy problem. Our algorithm uses O(pt) time and space, and its expected cost for the tth resource use converges to optimal as O(plog t=t), for any bounded probability distribution on the resource use times. Alternatively, using O(1) time and space, the algorithm almost converges to optimal. We describe the experimental results for the application of our algorithm to one of the motivating systems problems: the question of when to spindown a disk to save power in a mobile computer. Simulations using disk access traces obtained from an HP workstation environment suggest that our algorithm yields signi cantly improved power/response time performance over the non-adaptive 2-competitive algorithm which is optimal in the worst-case competitive analysis model. dc.language.iso en_US dc.publisher Springer Verlag dc.title Adaptive Disk Spindown via Optimal Rent-to-Buy in Probabilistic Environments dc.type Article kusw.kuauthor Vitter, Jeffrey Scott kusw.oastatus fullparticipation dc.identifier.doi 10.1007/PL00009249 kusw.oaversion Scholarly/refereed, author accepted manuscript kusw.oapolicy This item meets KU Open Access policy criteria. dc.rights.accessrights openAccess
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