Dynamic offloading for energy-aware scheduling in a mobile cloud

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. (C) 2022 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

Automated summary

Mobile cloud computing provides rich computational resources for mobile users, network operators, and cloud computing providers. Offloading applications to remote cloud resources can save energy in a dynamic mobile cloud computing environment. Our proposed algorithm outperforms other methods in energy consumption reduction and number of finished jobs.