Energy Efficient Task Offloading for Compute-intensive Mobile Edge Applications

In mobile edge computing (MEC) systems, offloading real-time and compute-intensive application tasks to remote edge servers is performed to relieve energy-constrained mobile devices of energy consuming computations. However, such practice often becomes counter-productive as transmission power requirements to offload such real-time tasks through wireless can make the mobile devices spend significant energy. In this paper, we propose an energy-efficient task offloading scheme for real-time and compute-intensive applications that optimizes energy consumption at mobile devices without violating such applications' strict latency requirements. In particular, for local energy savings at the mobile devices, we propose a Computation and Power Optimization (CPO) algorithm for optimal job partitioning. Then we propose a multi-device and multi-server task Joint Task Offloading Game (JTOG) algorithm in order to minimize the energy consumption for all mobile devices generating multiple tasks. Finally, using a realistic and detailed simulation, we prove that a tractable Nash Equilibrium always exists for the game that optimizes the energy savings of all mobile devices. We also show that the proposed JTOG algorithm performs significantly better than other default full task offloading schemes in terms of overall energy savings.