[1] Zhang Jing, Xia Weiwei, Huang Bonan, Zou Qian, et al. Joint resource allocation scheme based on evolutionary gamefor mobile edge computing [J]. Journal of Southeast University (English Edition), 2018, 34 (4): 415-422. [doi:10.3969/j.issn.1003-7985.2018.04.001]
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Joint resource allocation scheme based on evolutionary gamefor mobile edge computing(
)
移动边缘云计算中基于演进博弈的联合资源分配算法
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 34
- Issue:
- 2018 4
- Page:
- 415-422
- Research Field:
- Information and Communication Engineering
- Publishing date:
- 2018-12-20
Info
- Title:
- Joint resource allocation scheme based on evolutionary gamefor mobile edge computing
- 移动边缘云计算中基于演进博弈的联合资源分配算法
- Author(s):
- Zhang Jing; Xia Weiwei; Huang Bonan; Zou Qian; Shen Lianfeng
- National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
- Keywords:
- mobile edge computing; service provider selection; joint resource allocation; evolutionary game
- PACS:
- TN929.5
- DOI:
- 10.3969/j.issn.1003-7985.2018.04.001
- Abstract:
- To satisfy mobile terminals’(MTs)offloading requirements and reduce MTs’ cost, a joint cloud and wireless resource allocation scheme based on the evolutionary game(JRA-EG)is proposed for overlapping heterogeneous networks in mobile edge computing environments. MTs that have tasks offloading requirements in the same service area form a population. MTs in one population acquire different wireless and computation resources by selecting different service providers(SPs). An evolutionary game is formulated to model the SP selection and resource allocation of the MTs. The cost function of the game consists of energy consumption, time delay and monetary cost. The solutions of evolutionary equilibrium(EE)include the centralized algorithm based on replicator dynamics and the distributed algorithm based on Q-learning. Simulation results show that both algorithms can converge to the EE rapidly. The differences between them are the convergence speed and trajectory stability. Compared with the existing schemes, the JRA-EG scheme can save more energy and have a smaller time delay when the data size becomes larger. The proposed scheme can schedule the wireless and computation resources reasonably so that the offloading cost is reduced efficiently.
- 在移动边缘云计算系统中重复覆盖的异构网络场景下, 为了满足移动终端的任务卸载需求, 同时降低终端任务卸载代价, 提出基于演进博弈的云资源和计算资源联合分配方案(JRA-EG).同一个区域内具有任务卸载需求的终端形成一个种群, 种群中终端通过选择不同的服务点(SPs)获得不同的无线资源和计算资源.为了建模与分析服务点选择与资源分配, 建立了演进博弈模型.博弈的代价函数包括能耗代价、时延代价和经济代价.分别提出了基于复制动态的集中式算法和基于Q-learning的分布式算法求解演进均衡.仿真结果表明, 所提的2种算法均能快速收敛至均衡解.与已有算法相比, JRA-EG方案节省了终端消耗能量, 同时也降低了任务卸载时延.提出的方案能合理调度云资源和无线资源, 从而有效降低终端的任务卸载代价.
References:
[1] Khan A U R, Othman M, Madani S A, et al. A survey of mobile cloud computing application models[J]. IEEE Communications Surveys & Tutorials, 2014, 16(1): 393-413.DOI:10.1109/surv.2013.062613.00160.
[2] Barbarossa S, Sardellitti S, Di Lorenzo P. Communicating while computing: Distributed mobile cloud computing over 5G heterogeneous networks[J]. IEEE Signal Processing Magazine, 2014, 31(6):45-55.DOI:10.1109/msp.2014.2334709.
[3] Niyato D, Wang P, Hossain E, et al. Game theoretic modeling of cooperation among service providers in mobile cloud computing environments [C]// 2012 IEEE Wireless Communications and Networking Conference. Shanghai, China, 2012:3128-3133. DOI: 10.1109/WCNC.2012.6214343.
[4] Wang C M, Liang C C, Yu F R, et al. computation offloading and resource allocation in wireless cellular networks with mobile edge computing [J]. IEEE Transactions on Wireless Communications, 2017, 16(8):4924-4938. DOI: 10.1109/TWC.2017.2703901.
[5] Rimal B P, Van D P, Maier M. Mobile-edge computing vs. centralized cloud computing in fiber-wireless access networks [C]//2016 IEEE Conference on Computer Communications Workshops. San Francisco, CA, USA, 2016:16285777. DOI: 10.1109/INFCOMW.2016.7562226.
[6] Mao Y Y, Zhang J, Letaief K B. Dynamic computation offloading for mobile-edge computing with energy harvesting devices[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(12):3590-3605. DOI:10.1109/jsac.2016.2611964.
[7] Xu J, Chen L X, Ren S L. Online learning for offloading andautoscaling in energy harvesting mobile edge computing[J]. IEEE Transactions on Cognitive Communications and Networking, 2017, 3(3): 361-373. DOI:10.1109/tccn.2017.2725277.
[8] Wang C M, Yu F R, Liang C C, et al. Joint computation offloading and interference management in wireless cellular networks with mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2017, 66(8): 7432-7445. DOI:10.1109/tvt.2017.2672701.
[9] de la Roche G, Valcarce A, Lopez-Perez D, et al. Access control mechanisms for femtocells[J]. IEEE Communications Magazine, 2010, 48(1): 33-39. DOI:10.1109/mcom.2010.5394027.
[10] Zhu K, Hossain E, Niyato D. Pricing, spectrum sharing, and service selection in two-tier small cell networks: A hierarchical dynamic game approach[J]. IEEE Transactions on Mobile Computing, 2014, 13(8): 1843-1856. DOI:10.1109/tmc.2013.96.
[11] Mach P, Becvar Z. Mobile edge computing: A survey on architecture and computation offloading[J]. IEEE Communications Surveys & Tutorials, 2017, 19(3): 1628-1656. DOI:10.1109/comst.2017.2682318.
[12] Niyato D, Hossain E. Dynamics of network selection in heterogeneous wireless networks: An evolutionary game approach[J]. IEEE Transactions on Vehicular Technology, 2009, 58(4):2008-2017. DOI:10.1109/tvt.2008.2004588.
[13] Wei Q L, Lewis F L, Sun Q Y, et al. Discrete-time deterministic Q-learning: A novel convergence analysis[J]. IEEE Transactions on Cybernetics, 2017, 47(5): 1224-1237. DOI:10.1109/tcyb.2016.2542923.
[14] Mwanje S S, Schmelz L C, Mitschele-Thiel A. Cognitive cellular networks: A Q-learning framework for self-organizing networks[J]. IEEE Transactions on Network and Service Management, 2016, 13(1): 85-98. DOI:10.1109/tnsm.2016.2522080.
[15] Do C T, Tran N H, Tran D H, et al. Toward service selection game in a heterogeneous market cloud computing [C]//2015 IFIP/IEEE International Symposium on Integrated Network Management(IM). Ottawa, ON, Canada, 2015:44-52. DOI: 10.1109/INM.2015.7140275.
[16] Yan S, Peng M G, Abana M A, et al. An evolutionary game for user access mode selection in fog radio access networks[J]. IEEE Access, 2017, 5: 2200-2210. DOI:10.1109/access.2017.2654266.
Memo
- Memo:
-
Biographies: Zhang Jing(1993—), female, Ph.D. candidate; Xia Weiwei(corresponding author), female, doctor, associate professor, wwxia@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.61741102, 61471164).
Citation: .Zhang Jing, Xia Weiwei, Huang Bonan, et al. Joint resource allocation scheme based on evolutionary game for mobile edge computing[J].Journal of Southeast University(English Edition), 2018, 34(4):415-422.DOI:10.3969/j.issn.1003-7985.2018.04.001.