Adaptive Disk Spindown via Optimal Rent-to-Buy in Probabilistic Environments

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 rentto -buy problem is motivated by important systems applications, specifically, 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 efficient algorithm for the rent-to-buy problem and evaluate its practical merit for the disk spindown scenario via simulation..