Dynamic offloading for energy-aware scheduling in a mobile cloud
Junwen Lu, Yongsheng Hao, Kesou Wu, Yumin Chen, Qin Wang
Abstract
Mobile cloud computing (MCC) brings rich computational resources to mobile users, network operators, and cloud computing providers. The battery capacity of mobile devices poses several complex challenges, hence it is necessary to save energy by offloading applications to the remote cloud resources, especially when the scheduling is in a dynamic mobile cloud computing environment. To make a tradeoff decision involving energy consumption, deadline, and the system load, we proposed an iterated greedy taboo-mechanism algorithm (IGTMA) to solve the above issues in MCC environment. Compared to state-of-art approaches such as Adaptive First Come First Served (AFCFS), Minimize Execution Time (MINET), and tradeoff decisions for code offloading (TRADEOFF), the simulation experiment results show that our proposed IGTMA reduces energy consumption and enhances the number of finished jobs.