A semi-Markovian decision process based control method for offloading tasks from mobile devices to the cloud

The finite and rather small battery energy capacity in today's mobile devices has limited the functionality that can be integrated into these platforms or the performance and quality of applications that can be delivered to the users. In the last few years, there is a trend toward offloading certain computation-intensive and latency-tolerant local applications and service requests to a mobile cloud computing (MCC) system so as to save the precious battery life while providing the services requested by the users. Each mobile application can be thought of as a sequence of tasks that are executed locally or remotely. In this paper, the problem of optimal task dispatch, transmission, and execution onto the MCC system is considered. To achieve a good balance between the application execution time and power consumption, dynamic voltage and frequency scaling (DVFS) is applied to the local processor in the mobile device, while the transmitter can choose among multiple modulation schemes and bit rates. The rate capacity effect of a battery and power conversion losses in the mobile device are also accounted for so as to have a more realistic model of the remaining battery life. The mobile device is modeled as a semi-Markov decision process (SMDP) and the optimization problem to set the DVFS level and the transmission rate is effectively solved by linear programming combined with a one-dimensional heuristic search. Experimental results show that the proposed algorithm consistently outperforms some baseline algorithms.