超密集网络中基于移动边缘计算的任务卸载和资源优化
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摘要
移动边缘计算(MEC)通过在无线网络边缘为用户提供计算能力,来提高用户的体验质量。然而,MEC的计算卸载仍面临着许多问题。该文针对超密集组网(UDN)的MEC场景下的计算卸载,考虑系统总能耗,提出卸载决策和资源分配的联合优化问题。首先采用坐标下降法制定了卸载决定的优化方案。同时,在满足用户时延约束下采用基于改进的匈牙利算法和贪婪算法来进行子信道分配。然后,将能耗最小化问题转化为功率最小化问题,并将其转化为一个凸优化问题得到用户最优的发送功率。仿真结果表明,所提出的卸载方案可以在满足用户不同时延的要求下最小化系统能耗,有效地提升了系统性能。
Mobile Edge Computing(MEC) improves the quality of users experience by providing users with computing capabilities at the edge of the wireless network. However, computing offloading in MEC still faces some problems. In this paper, a joint optimization problem of offloading decision and resource allocation is proposed for the computation offloading problem in Ultra-Dense Networks(UDN) with MEC. To solve this problem, firstly, the coordinate descent method is used to formulate the optimization scheme for the offloading decision. Meanwhile, the improved Hungarian algorithm and greedy algorithm are used to allocate the channels to meet the user's delay requirements. Finally, the problem of minimizing energy consumption is converted into a problem of minimizing power. Then it is converted into a convex optimization problem to get the user's optimal transmission power. Simulation results show that the proposed scheme can minimize the energy consumption of the system while satisfying the users' different delay requirements, and improve effectively the performance of the system.
Mobile Edge Computing(MEC) improves the quality of users experience by providing users with computing capabilities at the edge of the wireless network. However, computing offloading in MEC still faces some problems. In this paper, a joint optimization problem of offloading decision and resource allocation is proposed for the computation offloading problem in Ultra-Dense Networks(UDN) with MEC. To solve this problem, firstly, the coordinate descent method is used to formulate the optimization scheme for the offloading decision. Meanwhile, the improved Hungarian algorithm and greedy algorithm are used to allocate the channels to meet the user's delay requirements. Finally, the problem of minimizing energy consumption is converted into a problem of minimizing power. Then it is converted into a convex optimization problem to get the user's optimal transmission power. Simulation results show that the proposed scheme can minimize the energy consumption of the system while satisfying the users' different delay requirements, and improve effectively the performance of the system.
引文
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[3]PAN Jianli and MCELHANNON J.Future edge cloud and edge computing for internet of things applications[J].IEEEInternet of Things Journal,2018,5(1):439-449.doi:10.1109/JIOT.2017.2767608.
[4]YANG Bin,MAO Guoqiang,DING Ming,et al.Dense small cell networks:from noise-limited to dense interferencelimited[J].IEEE Transactions on Vehicular Technology,2018,67(5):4262-4277.doi:10.1109/TVT.2018.2794452.
[5]GE Xiaohu,TU Song,MAO Guoqiang,et al.5G ultra-dense cellular networks[J].IEEE Wireless Communications,2016,23(1):72-79.doi:10.1109/MWC.2016.7422408.
[6]YANG Lichao,ZHANG Heli,LI Ming,et al.Mobile edge computing empowered energy efficient task offloading in5G[J].IEEE Transactions on Vehicular Technology,2018,67(7):6398-6409.doi:10.1109/TVT.2018.2799620.
[7]ZHANG Jiao,HU Xiping,NING Zhaolong,et al.Energylatency tradeoff for energy-aware offloading in mobile edge computing networks[J].IEEE Internet of Things Journal,2018,5(4):2633-2645.doi:10.1109/JIOT.2017.2786343.
[8]LIU Jianhui and ZHANG Qi.Offloading schemes in mobile edge computing for ultra-reliable low latency communications[J].IEEE Access,2018,6:12825-12837.doi:10.1109/ACCESS.2018.2800032.
[9]MAO Yuyi,ZHANG Jun,SONG S H,et al.Stochastic joint radio and computational resource management for multiuser mobile-edge computing systems[J].IEEE Transactions on Wireless Communications,2017,16(9):5994-6009.doi:10.1109/TWC.2017.2717986.
[10]TI N T and LE Longbao.Computation offloading leveraging computing resources from edge cloud and mobile peers[C].Proceedings of 2017 IEEE International Conference on Communications,Paris,France,2017:1-6.doi:10.1109/ICC.2017.7997138.
[11]ZHAO Pengtao,TIAN Hui,QIN Cheng,et al.Energysaving offloading by jointly allocating radio and computational resources for mobile edge computing[J].IEEE Access,2017(5):11255-11268.doi:10.1109/ACCESS.2017.2710056.
[12]ZHANG Jing,XIA Weiwei,YAN Feng,et al.Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing[J].IEEE Access,2018,6:19324-19337.doi:10.1109/ACCESS.2018.2819690.
[13]GUO Jun,ZHANG Heli,YANG Lichao,et al.Decentralized computation offloading in mobile edge computing empowered small-cell networks[C].Proceedings of 2017IEEE Globecom Workshops,Singapore,Singapore,2017:1-6.doi:10.1109/GLOCOMW.2017.8269049.
[14]RANADHEERA S,MAGHSUDI S,and HOSSAIN E.Computation offloading and activation of mobile edge computing servers:a minority game[J].IEEE Wireless Communications Letters,2018,7(5):688-691.doi:10.1109/LWC.2018.2810292.
[15]WANG Chenmeng,YU F R,LIANG Chengchao,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.
[16]DINH T Q,TANG Jianhua,LA Q D,et al.Offloading in mobile edge computing:task allocation and computational frequency scaling[J].IEEE Transactions on Communications,2017,65(8):3571-3584.doi:10.1109/TCOMM.2017.2699660.
[17]RAM S S,VEERAVALLI V V,and NEDIC A.Distributed non-autonomous power control through distributed convex optimization[C].Proceedings of IEEE INFOCOM 2009,Rio de Janeiro,Brazil,2009:3001-3005.doi:10.1109/INFCOM.2009.5062275.
[18]LIU Peng,LI Jiandong,LI Hongyan,et al.Convex optimisation-based joint channel and power allocation scheme for orthogonal frequency division multiple access networks[J].IET Communications,2015,9(1):28-32.doi:10.1049/iet-com.2014.0409.
[19]3GPP organizational parthners.Evolved universal terrestrial radio access(E-UTRA);Further advancements for E-UTRA physical layer aspects(Release 9),document TS 36.814,3GPP[OL].http://www.3gpp.org/ftp/,2012.