Adaptive code offloading for mobile cloud applications: exploiting fuzzy sets and evidence-based learning

Abstract

Mobile cloud computing is arising as a prominent domain that is seeking to bring the massive advantages of the cloud to the resource constrained smartphones, by following a delegation or offloading criteria. In a delegation model, a mobile device consumes services from multiple clouds by efficiently utilizing solutions like middleware. In the offloading model, a mobile application is partitioned and analyzed so that the most computational expensive operations at code level can be identified and offloaded for remote processing. While code offloading is studied extensively for the development of mobile cloud applications, much of the advantages of cloud computing are still left unexploited and poorly considered in these approaches. Cloud computing may introduce many other dynamic variables like performance metrics, parallelization of tasks, elasticity etc., to current code offloading models that could affect the overall offloading decision process. To address this, we propose a fuzzy decision engine for code offloading, that considers both mobile and cloud variables. The cloud parameters and rules are introduced asynchronously to the mobile, using notification services. The paper also proposes a strategy to enrich the offloading decision process with evidence-based learning methods, by exploiting cloud processing capabilities over code offloading traces.