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dc.titleAdaptive disk spindown via optimal rent-to-buy in probabilistic environments
dc.contributor.authorKrishnan, P.
dc.contributor.authorLong, P.M.
dc.contributor.authorVitter, J.S.
dc.identifier.citationKrishnan, P.,Long, P.M.,Vitter, J.S. (1999). Adaptive disk spindown via optimal rent-to-buy in probabilistic environments. Algorithmica (New York) 23 (1) : 31-56. ScholarBank@NUS Repository.
dc.description.abstractIn 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, specifically, problems arising from deciding when to spindown disks to conserve energy in mobile computers [4], [13], [15], thread blocking decisions during lock acquisition in multiprocessor applications [7], and virtual circuit holding times in IP-over-ATM networks [11], [19]. We develop a provably optimal and computationally efficient algorithm for the rent-to-buy problem. Our algorithm uses O(√t) time and space, and its expected cost for the tth resource use converges to optimal as O(√log 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 significantly improved power/response time performance over the nonadaptive 2-competitive algorithm which is optimal in the worst-case competitive analysis model.
dc.subjectDisk spindown
dc.subjectMachine learning
dc.subjectMobile computing
dc.subjectMultiprocessor spin/block
dc.subjectOn-line algorithms
dc.subjectPower conservation
dc.subjectVirtual circuit holding time
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleAlgorithmica (New York)
Appears in Collections:Staff Publications

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