Cost-Efficient, Utility-Based Caching of Expensive Computations in the Cloud

We present a model and system for deciding on computing versus storage trade-offs in the Cloud using von Neumann-Morgenstern lotteries. We use the decision model in a video-on-demand system providing cost-efficient transcoding and storage of videos. Video transcoding is an expensive computational process that converts a video from one format to another. Video data are large enough to cause concern over rising storage costs. In the general case, our work is of interest when dealing with expensive computations that generate large results that can be cached for future use. Solving the decision problem entails solving two sub-problems: how long to store cached objects and how many requests we can expect for a particular object in that duration. We compare the proposed approach to always storing and to our previous approach over one year using discrete-event simulations. We observe a 72% cost reduction compared to always storing and a 13% reduction compared to our previous approach. This reduction in cost stems from the proposed approach storing fewer unpopular objects when it does not regard it as cost-efficient to do so.