On Exploiting Dynamic Execution Patterns for Workload Offloading in Mobile Cloud Applications

Mobile Cloud Computing (MCC) bridges the gap between limited capabilities of mobile devices and the increasing users´ demand of mobile multimedia applications, by offloading the computational workloads from local devices to the remote cloud. Current MCC research focuses on making offloading decisions over different methods of a MCC application, but may inappropriately increase the energy consumption if having transmitted a large amount of program states over expensive wireless channels. Limited research has been done on avoiding such energy waste by exploiting the dynamic patterns of applications´ run-time execution for workload offloading. In this paper, we adaptively offload the local computational workload with respect to the run-time application dynamics. Our basic idea is to formulate the dynamic executions of user applications using a semi-Markov model, and to further make offloading decisions based on probabilistic estimations of the offloading operation´s energy saving. Such estimation is motivated by experimental investigations over practical smart phone applications, and then builds on analytical modeling of methods´ execution times and offloading expenses. Systematic evaluations show that our scheme significantly improves the efficiency of workload offloading compared to existing schemes over various smart phone applications.