Partial offloading strategy for mobile edge computing considering mixed overhead of time and energy
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MULTI-ACCESS EDGE COMPUTING ENABLED INTERNET OF THINGS
Partial offloading strategy for mobile edge computing considering mixed overhead of time and energy Qiang Tang1 • Haimei Lyu1 • Guangjie Han2,3,4
•
Jin Wang1 • Kezhi Wang5
Received: 11 January 2019 / Accepted: 30 July 2019 Ó Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract Mobile edge computing (MEC) utilizes wireless access network to provide powerful computing resources for mobile users to improve the user experience, which mainly includes two aspects: time and energy consumption. Time refers to the latency consumed to process user tasks, while energy consumption refers to the total energy consumed in processing tasks. In this paper, the time and energy consumption in user experience are weighted as a mixed overhead and then optimized jointly. We formulate a mixed overhead of time and energy (MOTE) minimization problem, which is a nonlinear programming problem. In order to solve this problem, the block coordinate descent method to deal with each variable step by step is adopted. We further analyze the minimum value of delay parameters in the model, and examine two special cases: 1-offloading and 0-offloading. In 1-offloading, all the task data is offloaded to MEC server, and no data offloaded in 0-offloading. The necessary and sufficient conditions for the existence of two special cases are also deduced. Besides, the multi-user situation is also discussed. In the performance evaluation, we compare MOTE with other offloading schemes, such as exhaustive strategy and Monte Carlo simulation method-based strategy to evaluate the optimality. The simulation results show that MOTE always achieves the minimal overhead compared to other algorithms. Keywords Full granularity Partial offloading Mixed overhead Mobile edge computing
1 Introduction In recent years, the fifth generation (5G) of mobile communication systems has emerged to cope with the explosive growth of mobile data traffic, massive device & Guangjie Han [email protected] 1
Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
2
School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
3
Department of Information and Communication Systems, Hohai University, Changzhou 213022, China
4
State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, Beijing, China
5
Department of Computer and Information Sciences, Northumbria University, Newcastle, UK
connections and new services, etc. [1, 2]. Many typical application scenarios involve the 5G technology, such as the applications in dense residential areas, offices, stadiums, subways, etc., and the various applications could be augmented reality, virtual reality, ultra-high definition video, cloud storage, etc., which in general
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