Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things

• Published in: IEEE transactions on cloud computing Vol. 9; no. 3; pp. 1050 - 1060
• Main Authors: Chen, Ying, Zhang, Ning, Zhang, Yongchao, Chen, Xin, Wu, Wen, Shen, Xuemin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE Computer Society 01-07-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Computation offloading; Decision analysis; Decision theory; dynamic offloading; Edge computing; Energy consumption; energy efficient offloading; Heuristic algorithms; Internet of Things; Mobile computing; mobile edge computing; Optimization; Optimization techniques; Polynomials; Queues; Solution space; Task analysis; Wireless communication
• ISSN: 2168-7161; 2168-7161; 2372-0018
• DOI: 10.1109/TCC.2019.2898657

With proliferation of computation-intensive Internet of Things (IoT) applications, the limited capacity of end devices can deteriorate service performance. To address this issue, computation tasks can be offloaded to the Mobile Edge Computing (MEC) for processing. However, it consumes considerable energy to transmit and process these tasks. In this paper, we study the energy efficient task offloading in MEC. Specifically, we formulate it as a stochastic optimization problem, with the objective of minimizing the energy consumption of task offloading while guaranteeing the average queue length. Solving this offloading optimization problem faces many technical challenges due to the uncertainty and dynamics of wireless channel state and task arrival process, and the large scale of solution space. To tackle these challenges, we apply stochastic optimization techniques to transform the original stochastic problem into a deterministic optimization problem, and propose an energy efficient dynamic offloading algorithm called EEDOA. EEDOA can be implemented in an online manner to make the task offloading decisions with polynomial time complexity. Theoretical analysis is provided to demonstrate that EEDOA can approximate the minimal transmission energy consumption while still bounding the queue length. Experiment results are presented which show the EEDOA's effectiveness.