Energy-efficient multisite offloading policy using Markov decision process for mobile cloud computing

Research paper by Mati B. Terefe, Heezin Lee, Nojung Heo, Geoffrey C. Fox, Sangyoon Oh

Indexed on: 20 Mar '16Published on: 22 Oct '15Published in: Pervasive and Mobile Computing


Mobile systems, such as smartphones, are becoming the primary platform of choice for a user’s computational needs. However, mobile devices still suffer from limited resources such as battery life and processor performance. To address these limitations, a popular approach used in mobile cloud computing is computation offloading, where resource-intensive mobile components are offloaded to more resourceful cloud servers. Prior studies in this area have focused on a form of offloading where only a single server is considered as the offloading site. Because there is now an environment where mobile devices can access multiple cloud providers, it is possible for mobiles to save more energy by offloading energy-intensive components to multiple cloud servers. The method proposed in this paper differentiates the data- and computation-intensive components of an application and performs a multisite offloading in a data and process-centric manner. In this paper, we present a novel model to describe the energy consumption of a multisite application execution and use a discrete time Markov chain (DTMC) to model fading wireless mobile channels. We adopt a Markov decision process (MDP) framework to formulate the multisite partitioning problem as a delay-constrained, least-cost shortest path problem on a state transition graph. Our proposed Energy-efficient Multisite Offloading Policy (EMOP) algorithm, built on a value iteration algorithm (VIA), finds the efficient solution to the multisite partitioning problem. Numerical simulations show that our algorithm considers the different capabilities of sites to distribute appropriate components such that there is a lower energy cost for data transfer from the mobile to the cloud. A multisite offloading execution using our proposed EMOP algorithm achieved a greater reduction on the energy consumption of mobiles when compared to a single site offloading execution.

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