Dynamic Task Offloading for Mobile Edge Computing with Green Energy

被引:0
作者
Ma H. [1 ]
Chen X. [1 ]
Zhou Z. [1 ]
Yu S. [1 ]
机构
[1] School of Data and Computer Science, Sun Yat-sen University, Guangzhou
来源
Chen, Xu (chenxu35@mail.sysu.edu.cn) | 1823年 / Science Press卷 / 57期
基金
中国国家自然科学基金;
关键词
D2D collaboration; Energy harvesting; Incentive-awareness; Mobile edge computing (MEC); Task offloading;
D O I
10.7544/issn1000-1239.2020.20200184
中图分类号
学科分类号
摘要
Mobile edge computing (MEC) has recently emerged to fulfill the computation demands of richer applications, and provide better experience for resource-hungry Internet-of-Things (IoT) devices at the edge of mobile networks. It is readily acknowledged that edge infrastructures are less capable of improving power usage efficiency (PUE) and integrating renewable energy. Besides, due to the limited battery capacities of IoT devices, the task execution would be interrupted when the battery runs out. Therefore, it is crucial to use green energy to prolong the battery life-time. Moreover, IoT devices can share computation and communication resources dynamically and beneficially among each other. Therefore, we develop an efficient task offloading strategy in order to improve PUE of edge server as well as achieving green computing. We also propose a green task offloading framework which leverages energy harvesting (EH) and device-to-device communication (D2D). Our framework aims at minimizing the long-term grid power energy consumption of edge server and cloud resource rental costs for task executions of all EH IoT devices. Meanwhile, the incentive constraints of preventing the over-exploiting behaviors should be considered, since they harm devices' motivation for collaboration. To address the uncertain future system information, such as the availability of renewable energy, we resort to Lyapunov optimization technique to propose an online task offloading algorithm, in which the decisions only depend on system current state information. The implementation of this algorithm only requires to solve a deterministic problem in each time slot, for which the core idea is to transform the task offloading problem of each time slot into a graph matching problem and get the approximate optimal solution by calling Edmonds's Blossom algorithm. Rigorous theoretical analysis and extensive evaluations demonstrate the superior performance of the proposed scheme. © 2020, Science Press. All right reserved.
引用
收藏
页码:1823 / 1838
页数:15
相关论文
共 38 条
[21]  
Gai Keke, Qiu Meikang, Zhao Hui, Et al., Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing, Journal of Network & Computer Applications, 59, pp. 46-54, (2016)
[22]  
Neely M J., Stochastic network optimization with application to communication and queueing systems, Synthesis Lectures on Communication Networks, 3, 1, pp. 1-211, (2010)
[23]  
Edmonds J, Johnson E L., Matching, Euler tours and the Chinese postman, Mathematical Programming, 5, 1, pp. 88-124, (1973)
[24]  
Sun Haijian, Zhou Fuhui, Hu Rose Qingyang, Joint offloading and computation energy efficiency maximization in a mobile edge computing system, IEEE Transactions on Vehicular Technology, 68, 3, pp. 3052-3056, (2019)
[25]  
Pan Yijin, Chen Ming, Yang Zhaohui, Et al., Energy-efficient noma-based mobile edge computing offloading, IEEE Communications Letters, 23, 2, pp. 310-313, (2019)
[26]  
Feng Jie, Pei Qingqi, Yu Richard F, Et al., Computation offloading and resource allocation for wireless powered mobile edge computing with latency constraint, IEEE Wireless Communications Letters, 8, 5, pp. 1320-1323, (2019)
[27]  
Yang Xiaotong, Yu Xueyong, Huang Hao, Et al., Energy efficiency based joint computation offloading and resource allocation in multi-access MEC systems, IEEE Access, 7, pp. 117054-117062, (2019)
[28]  
Wang Feng, Xu Jie, Wang Xin, Et al., Joint offloading and computing optimization in wireless powered mobile-edge computing systems, IEEE Transactions on Wireless Communications, 17, 3, pp. 1784-1797, (2018)
[29]  
Ning Zhaolong, Dong Peiran, Kong Xiangjie, Et al., A cooperative partial computation offloading scheme for mobile edge computing enabled Internet of things, IEEE Internet of Things Journal, 6, 3, pp. 4804-4814, (2019)
[30]  
Xu Jie, Chen Lixing, Ren Shaolei, Online learning for offloading and autoscaling in energy harvesting mobile edge computing, IEEE Transactions on Cognitive Communications and Networking, 3, 3, pp. 361-373, (2017)