|本期目录/Table of Contents|

[1]周文晨,方维维,李阳阳,等.移动边缘计算中分布式的设备发射功率优化算法[J].西安交通大学学报,2018,52(12):121-127.[doi:10.7652/xjtuxb201812018]
 ZHOU Wenchen,FANG Weiwei,LI Yangyang,et al.A Distributed Algorithm for Transmit Power Optimization in Mobile Edge Computing[J].Journal of Xi'an Jiaotong University,2018,52(12):121-127.[doi:10.7652/xjtuxb201812018]
点击复制

移动边缘计算中分布式的设备发射功率优化算法(PDF)

《西安交通大学学报》[ISSN:0253-987X/CN:61-1069/T]

卷:
52
期数:
2018年第12期
页码:
121-127
栏目:
出版日期:
2018-12-10

文章信息/Info

Title:
A Distributed Algorithm for Transmit Power Optimization in
Mobile Edge Computing
作者:
周文晨1方维维1李阳阳2薛峰1王子岳1
1.北京交通大学计算机与信息技术学院,100044,北京;2.中国电子科学研究院创新中心,100041,北京
Author(s):
ZHOU Wenchen1FANG Weiwei1LI Yangyang2XUE Feng1WANG Ziyue1
1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
2. Innovation Center, China Academy of Electronics and Information Technology, Beijing 100041, China
关键词:
移动边缘计算计算卸载马尔可夫近似
Keywords:
mobile edge computing computation offloading Markov approximation
分类号:
TN915.6
DOI:
10.7652/xjtuxb201812018
摘要:
针对大规模移动边缘计算网络架构中的用户设备计算卸载时所需的通信和计算资源难以协同优化的问题,提出了一种基于马尔可夫近似的分布式发射功率优化算法。基于香农定理和链路传输特性,将用户功率最小化策略建模成组合优化模型,通过LogSumExp函数将目标模型转化为最小权重配置的近似问题;针对该近似问题,提出了马尔可夫状态跳转的规则和分布式的设备自调节机制以实现高效求解。实验结果表明:与随机优化算法相比,该算法的系统用户设备发射总功率优化效果提升了78.5%,在给定场景下,穷举搜索最优解的计算复杂度可达410,而该算法仅需要迭代优化130次即可逼近最优解,能够有效减少通信和计算时延,确保发射功率的调整结果快速向最优目标收敛。
Abstract:
A novel distributed algorithm for transmit power optimization based on Markov approximation framework is proposed to solve the problem that it is difficult to collaboratively optimize communication and computing resources for computing task offloading of devices in a largescale mobile edge computing’s network architecture. Strategy for minimizing power consumption of user devices is modeled as a combinatorial optimization model based on Shannon’s theorem and link transmission characteristics, and then the problem is transformed into an approximation problem for minimum weight configuration by the LogSumExp approximation rule.Then, the approximation problem is efficiently solved by proposing the Markov state transition rule and the distributed selfadjustment mechanism. Experimental results show that the optimized result of total power consumption of the devices by using the proposed algorithm is 78.5% better than that by using the random method. The proposed algorithm approaches the optimal solution after only 130 iterations in a given scenario, while the complexity of the exhaustive search’s optimal solution is up to 410. The proposed algorithm effectively reduces the delay and ensures that the adjustment result of the transmission power quickly converges to the optimal target.

参考文献/References:

[1]FANG Weiwei, YAO Xuening, ZHAO Xiaojie, et al. A stochastic control approach to maximize profit on service provisioning for mobile cloudlet platforms [J]. IEEE Transactions on Systems, Man and Cybernetics, 2018, 48(4): 522534.
[2]Cisco. Cisco visual networking index: global mobile data traffic forecast update [EB/OL]. (20180122)[20180307]. https: ∥www. cisco. com/c/en/us/solutions/collateral/serviceprovider/visualnetworkingindexvni/mobilewhitepaperc11520862. pdf.
[3]何秀丽, 任智源, 史晨华, 等. 面向医疗大数据的云雾网络及其分布式计算方案 [J]. 西安交通大学学报, 2016, 50(10): 7177.
HE Xiuli, REN Zhiyuan, SHI Chenhua, et al. A cloud and fog network architecture for medical big data and its distributed computing scheme [J]. Journal of Xi’an Jiaotong University, 2016, 50(10): 7177.
[4]田辉, 范绍帅, 吕昕晨, 等. 面向5G需求的移动边缘计算 [J]. 北京邮电大学学报, 2017, 40(2): 110.
TIAN Hui, FAN Shaoshuai, L Xinchen, et al. Mobile edge computing for 5G requirements [J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(2): 110.
[5]FANG Weiwei, ZHAO Xiaojie, AN Yuan, et al. Optimal scheduling for energy harvesting mobile sensing devices [J]. Computer Communications, 2016, 75(C): 6270.
[6]李健, 黄庆佳, 刘一阳, 等. 云计算环境下的大规模图状数据处理任务调度算法 [J]. 西安交通大学学报, 2012, 46(12): 116122.
LI Jian, HUANG Qingjia, LIU Yiyang, et al. A task scheduling algorithm for large graph processing cloud in computing [J]. Journal of Xi’an Jiaotong University, 2012, 46(12): 116122.
[7]MAO Yuyi, ZHANG Jun, SONG S H, et al. Powerdelay tradeoff in multiuser mobileedge computing systems [C]∥ Proceedings of the 2016 IEEE International Symposium on Information Theory. Piscataway, NJ, USA: IEEE, 2015, 63(10): 38423855.
[8]LIU Juan, MAO Yuyi, ZHANG Jun, et al. Delayoptimal computation task scheduling for mobileedge computing systems [C]∥Proceedings of the 2016 IEEE International Symposium on Information Theory. Piscataway, NJ, USA: IEEE, 2016: 14511455.
[9]YOU Changsheng, HUANG Kaibin, HYUKJIN C, et al. Energyefficient resource allocation for mobileedge computation offloading [J]. IEEE Transactions on Wireless Communications, 2017, 16(3): 13971411.
[10]CHEN Siwei, GAN Xiaoying, FENG Xinxin, et al. Markov approximation for multiRAT selection [C]∥Proceedings of the IEEE International Conference on Communications. Piscataway, NJ, USA: IEEE, 2015: 30453050.
[11]BARBAROSSA S, SARDELLITTI S, LORENZO P D. Joint allocation of computation and communication resources in multiuser mobile cloud computing [C]∥Proceedings of the Signal Processing Advances in Wireless Communications. Piscataway, NJ, USA: IEEE, 2013: 2630.
[12]HAJIESMAILI M H, MAK L T, WANG Z, et al. Costeffective lowdelay cloud video conferencing [C]∥Proceedings of the International Conference on Distributed Computing Systems. Piscataway, NJ, USA: IEEE, 2015: 103112.
[13]LIEW S C, KAI C H, LEUNG H C, et al. Backoftheenvelope computation of throughput distributions in CSMA wireless networks [J]. IEEE Transactions on Mobile Computing, 2007, 9(9): 13191331.
[14]王大鸣, 陈松, 崔维嘉, 等. 多用户MIMOOFDM系统基于QoE效用函数的跨层资源分配 [J]. 通信学报, 2014, 35(9): 175183.
WANG Daming, CHEN Song, CUI Weijia, et al. QoE utility functionbased crosslayer resource allocation in multiuser MIMOOFDM systems [J]. Journal on Communications, 2014, 35(9): 175183.
[15]CHEN Minghua, LIEW S C, SHAO Ziyu, et al. Markov approximation for combinatorial network optimization [C]∥Proceedings of the Conference on Information Communications. Piscataway, NJ, USA: IEEE, 2010: 17831791.
[16]SHAO Ziyu, ZHANG Hao, CHEN Minghua, et al. Reverseengineering BitTorrent: a Markov approximation perspective [C]∥Proceedings of the 2012 IEEE Conference on Computer Communications. Piscataway, NJ, USA: IEEE, 2013: 29963000.
[17]FANG Hui, LIN Xia, LOK T M. Power allocation for multiuser cooperative communication networks under relayselection degree bounds [J]. IEEE Transactions on Vehicular Technology, 2012, 61(7): 29913001.
[18]JIANG Libin, WALRAND J. A distributed CSMA algorithm for throughput and utility maximization in wireless networks [C]∥Proceedings of the Conference on Communication, Control, and Computing. Piscataway, NJ, USA: IEEE, 2010: 960972.

备注/Memo

备注/Memo:
国家自然科学基金资助项目(61501022);中央高校基本科研业务费专项资金资助项目(2017JBM021);装备预研中国电科联合基金资助项目(6141B08020101)
更新日期/Last Update: