Energy-aware task assignment for mobile cyber-enabled applications in heterogeneous cloud computing

- We propose a novel approach for reducing the computation energy costs for heterogeneous MES in cloud systems. Our algorithm can intelligently assign the tasks to on-premise cores or remote cloud servers within an adaptive time period.
- We present a method of the adjustment that is designed to transfer sub-optimal solutions to optimal solutions at a high success rate.
- We propose a feasible solution to the proposed task assignment problem for heterogeneous MES that is a NP-hard problem. The proposed approach can be used in other application scenarios.

Recent remarkable growth of mobile computing has led to an exceptional hardware upgrade, including the adoption of the multiple core processors. Along with this trend, energy consumptions are becoming greater when the computation capacity or workload grows. As one of the solutions, using cloud computing can mitigate energy costs due to the centralized computation. However, simply offloading the workloads to the remote side cannot efficiently reduce the energy consumptions when the energy costs caused by wireless communications are greater than that of on mobile devices. In this paper, we focus on the energy-saving problem and consider the energy wastes when tasks are assigned to remote cloud servers or heterogeneous core processors. Our solution aims to reduce the total energy cost of the mobile heterogeneous embedded systems by a novel task assignment to heterogeneous cores and mobile clouds. The proposed model is called Energy-Aware Heterogeneous Cloud Management (EA-HCM) model and the main algorithm is Heterogeneous Task Assignment Algorithm (HTA2). Our experimental evaluations have proved that our approach is effective to save energy when deploying heterogeneous embedded systems in mobile cloud systems.