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

A4 Conference proceedings


Internal Authors/Editors


Publication Details

List of Authors: Benjamin Byholm, Fareed Jokhio, Adnan Ashraf, Sébastien Lafond, Johan Lilius, Ivan Porres
Editors: Masoud Daneshtalab, Marco Aldinucci, Ville Leppänen, Johan Lilius, and Mats Brorsson
Publication year: 2015
Publisher: IEEE Computer Society Conference Publishing Services (CPS)
Book title: 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing
Title of series: Euromicro International Conference on Parallel, Distributed, and Network-Based Processing
Number in series: 23
Start page: 505
End page: 513
ISBN: 978-1-4799-8490-9


Abstract

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.


Keywords

Cache storage, Decision theory, Markov processes, Simulation, Transcoding, Utility theory, Web services


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Last updated on 2019-15-12 at 03:21