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Such schedulers must predict when an offloaded computation will outperform one that is local by forecasting the local cost (execution time for computing locally) and remote cost (execution time for computing remotely and transmission time for the input\/output of the computation to\/from the remote system). Typically, this decision amounts to predicting the bandwidth between the local and remote systems to estimate these costs. Our framework unifies such decision models by formulating the problem as a statistical decision problem that can either be treated \u201cclassically\u201d or using a Bayesian approach. Using an implementation of this framework, we evaluate the efficacy of a number of different decision strategies (several of which have been employed by previous systems). Our results indicate that a Bayesian approach employing automatic change-point detection when estimating the prior distribution is the best performing approach.<\/jats:p>","DOI":"10.1177\/1094342007095289","type":"journal-article","created":{"date-parts":[[2008,10,22]],"date-time":"2008-10-22T06:38:02Z","timestamp":1224657482000},"page":"460-479","source":"Crossref","is-referenced-by-count":7,"title":["On the Efficacy of Computation Offloading Decision-Making Strategies"],"prefix":"10.1177","volume":"22","author":[{"given":"Selim","family":"Gurun","sequence":"first","affiliation":[{"name":"COMPUTER SCIENCE DEPARTMENT, UNIVERSITY OF CALIFORNIA,\rSANTA BARBARA,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rich","family":"Wolski","sequence":"additional","affiliation":[{"name":"COMPUTER SCIENCE DEPARTMENT, UNIVERSITY OF CALIFORNIA,\rSANTA 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