Yulun Cheng
ABSTRACT
The1 promising feature of Mobile edge computing (MEC) is to provide computation capabilities at the wireless access networks, so as to reduce latency and improve user experience. Besides, wireless virtualization is also an emerging solution to reduce deployment cost for future wireless networks. This paper investigates the joint allocation of task offloading and transmission power for wireless virtualization aided MEC. Firstly, to minimize energy consumption, a centralized allocation is proposed by using mixed integer nonlinear programming. Then, the formulated problem is equivalently solved by Lagrange dual decomposition. On the basis of that, a distributed algorithm is proposed, which can solve computation offloading and power allocation within each MEC server separately. The effectiveness of the proposed algorithm is evaluated by simulations, and it shows that the proposed distributed algorithm can minimize energy consumption while converges to the optimal solutions.
- X. Chen, L. Jiao, W. Li, and X. Fu. 2016. Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing. IEEE/ACM Transactions on Networking, 24, 2795--2808. Google Scholar
Digital Library
- W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu. 2016. Edge Computing: Vision and Challenges. IEEE Internet of Things Journal, 3, 637--646.Google ScholarCross Ref
- A. Ahmed, and E. Ahmed. 2016. A survey on mobile edge computing. In Proceedings of 2016 10th International Conference on Intelligent Systems and Control (Coimbatore, India), 7--8.Google Scholar
- Y. Mao, J. Zhang, and K. B. Letaief. 2016. Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices. IEEE Journal on Selected Areas in Communications, 34, 3590--3605. Google ScholarDigital Library
- ETSI. 2014. Mobile-edge computing: introductory technical white paper. ETSI White Paper.Google Scholar
- C. Liang, and F. R. Yu. 2015. Wireless Network Virtualization: A Survey, Some Research Issues and Challenges. IEEE Communications Surveys & Tutorials, 17, 358--380.Google Scholar
- X. Costa-Perez, J. Swetina, T. Guo, R. Mahindra, and S. Rangarajan. 2013. Radio access network virtualization for future mobile carrier networks. IEEE Communications Magazine, 51, 27--35.Google ScholarCross Ref
- Y. Cheng, L. Yang, and H. Zhu. 2017. Operator Profit-Aware Wireless Virtualization for Device-to-Device Communications Underlaying LTE Networks. IEEE Access, 5, 11668--11676.Google ScholarCross Ref
- J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. K. Soong, and J. C. Zhang. 2014. What Will 5G Be?. IEEE Journal on Selected Areas in Communications, 5, 1065--1082.Google ScholarCross Ref
- F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta, and P. Popovski. 2014. Five disruptive technology directions for 5G. IEEE Communications Magazine, 52, 74--80.Google ScholarCross Ref
- Y. Cheng, L. Yang, and H. Zhu. 2017. Virtual Resource Allocation in Virtualized Small Cell Networks with Physical-Layer Network Coding Aided Self-Backhauls. KSII Transactions on Internet & Information Systems, 11, 3841--3861.Google Scholar
- S. Sardellitti, G. Scutari, and S. Barbarossa. 2015. Joint optimization of radio and computational resources for multicell mobile-edge computing. IEEE Transactions on Signal and Information Processing over Networks, 1, 89--103.Google ScholarCross Ref
- Y. Wang, M. Sheng, X. Wang, L. Wang, and J. Li. 2016. Mobile-edge computing: partial computation offloading using dynamic voltage scaling. IEEE Transactions on Communications, 64, 4268--4282.Google Scholar
- O. Munoz, A. Pascual-Iserte, and J. Vidal. 2015. Optimization of radio and computational resources for energy efficiency in latency-constrained application offloading. IEEE Transactions on Vehicular Technology, 64, 4738--4755.Google ScholarCross Ref
- L. Yang, J. Cao, H. Cheng, and Y. Ji. 2015. Multi-user computation partitioning for latency sensitive mobile cloud applications. IEEE Transactions on Computers, 64, 2253--2266.Google ScholarCross Ref
- L. A. Wolsey, and G. L. Nemhauser. 1999. Integer and Combinatorial Optimization. Wiley-Interscience, 211--245, ISBN 0471359432.Google Scholar
- M. Chiang, C. Tan, D. P. Palomar, Daniel O'neill, and D. Julian. 2007. Power Control By Geometric Programming. IEEE Transactions on Wireless Communications, 6, 2640--2651. Google ScholarDigital Library
- IBM ILOG CPLEX v12.1: User's Manual for CPLEX. 2009. International Business Machines Corporation.Google Scholar
Index Terms
- Distributed Computation Offloading and Power Allocation for Wireless Virtualization Aided Mobile Edge Computing
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