移动边缘计算环境下的动态资源分配策略
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摘要
在通讯设备爆炸式增长的时代,移动边缘计算作为5G通讯技术的核心技术之一,对其进行合理的资源分配显得尤为重要。移动边缘计算的思想是把云计算中心下沉到基站部署(边缘云),使云计算中心更加靠近用户,以快速解决计算资源分配问题。但是,相对于大型的云计算中心,边缘云的计算资源有限,传统的虚拟机分配方式不足以灵活应对边缘云的计算资源分配问题。为解决此问题,提出一种根据用户综合需求变化的动态计算资源和频谱分配算法(DRFAA),采用"分治"策略,并将资源模拟成"流体"资源进行分配,以寻求较大的吞吐量和较低的传输时延。实验仿真结果显示,动态计算资源和频谱分配算法可以有效地降低用户与边缘云之间的传输时延,也可以提高边缘云的吞吐量。
In the era of explosive growth of communication equipment, mobile edge computing is a core 5 G communication technique, and it is important to allocate resources reasonably. The idea of mobile edge computing is to sink cloud computing centers to the base station deployment(edge cloud) and to bring cloud computing centers closer to users to quickly solve the problem of computing resource allocation. However, compared with large cloud computing centers, the computing resources of the edge cloud are limited, and the traditional virtual machine allocation method cannot flexibly deal with the problem of computing resource allocation of the edge cloud. To solve this problem, we propose a dynamic computing resource and spectrum allocation algorithm(DRFAA) based on users' comprehensive needs. It adopts the "divide and conquer" strategy, and simulates resources into "fluid" resources for allocation so as to seek for a larger throughput and a lower transmission delay. Simulation results show that the dynamic computing resource and spectrum allocation algorithm can effectively reduce the transmission delay between the user and the edge cloud, and also improve the throughput of the edge cloud.
In the era of explosive growth of communication equipment, mobile edge computing is a core 5 G communication technique, and it is important to allocate resources reasonably. The idea of mobile edge computing is to sink cloud computing centers to the base station deployment(edge cloud) and to bring cloud computing centers closer to users to quickly solve the problem of computing resource allocation. However, compared with large cloud computing centers, the computing resources of the edge cloud are limited, and the traditional virtual machine allocation method cannot flexibly deal with the problem of computing resource allocation of the edge cloud. To solve this problem, we propose a dynamic computing resource and spectrum allocation algorithm(DRFAA) based on users' comprehensive needs. It adopts the "divide and conquer" strategy, and simulates resources into "fluid" resources for allocation so as to seek for a larger throughput and a lower transmission delay. Simulation results show that the dynamic computing resource and spectrum allocation algorithm can effectively reduce the transmission delay between the user and the edge cloud, and also improve the throughput of the edge cloud.
引文
[1] Li Xiao-bin,Yin Chao.Cloud services oriented manufacturing execution system for production process[J].Computer Integrated Manufacturing Systems,2016,22(1):177-188.(in Chinese)
[2] Hsu Ching-Hsien,Wang Shang-guang,Zhang Yan,et al.Mobile edge computing[M].Beijing:Beijing University of Posts and Telecommunications Press,2017.
[3] Yu R,Ding J,Maharjan S,et al.Decentralized and optimal resource cooperation in geo-distributed mobile cloud computing[J].IEEE Transactions on Emerging Topics in Computing,2018,6(1):72-84.
[4] Li J,Zhu Y M,Hua Y Q,et al.Crowdsourcing sensing to smartphones:A randomized auction approach[C]//Proc of 2015 IEEE 23rd International Symposium on Quality of Service(IWQoS),2015:219-224.
[5] Pu L J,Chen X,Xu J D,et al.Crowd foraging:A QoS-oriented self-organized mobile crowdsourcing framework over opportunistic networks[J].IEEE Journal on Selected Areas in Communications,2017,35(4):848-862.
[6] Zhang H G,Liu B Y,Susanto H,et al.Incentive mechanism for proximity-based mobile crowed service systems[C]//Proc of the 35th Annual IEEE International Conference on Computer Communications,2016:1-9.
[7] Han Y Y,Wu H Y.Minimum-cost crowdsourcing with coverage guarantee in mobile opportunistic D2D networks[J].IEEE Transactions on Mobile Computing,2017,16(10):2806-2818.
[8] Xu Lei.Research on resource allocation for OFDM wireless network [D].Nanjing:Nanjing University of Aeronautics and Astronautics,2012.(in Chinese)
[9] Yu Tao,Zhou Xiao-tian.Virtual machine dynamic supply and allocation algorithm based on auction[J].Computer Engineering and Applications,2016,52(23):81-87.(in Chinese)
[10] Lu Di,Ma Jian-feng,Wang Yi-chuan,et al.Enhanced fairness-based multi-resource allocation algorithm for cloud computing[J].Journal of Xidian University,2014,41(3):162-168.
[11] Qian Fang.Research on resource allocation technology of cognitive OFDM network[D].Nanjing:Nanjing University of Science and Technology,2016.(in Chinese)
[12] Gen He-jing.Study on allocation of virtual machine instances and pricing mechanism based on auction theory [D].Yangzhou:Yangzhou University,2017.(in Chinese)
[1] 李孝斌,尹超.面向生产过程云服务的制造执行系统[J].计算机集成制造系统,2016,22(1):177-188.
[8] 徐雷.OFDM无线网络资源分配技术研究[D].南京:南京航空航天大学,2012.
[9] 于涛,周小天.基于拍卖的虚拟机动态供应和分配算法[J].计算机工程与应用,2016,52(23):81-87.
[10] 卢笛,马建峰,王一川,等.云计算下保障公平性的多资源分配算法[J].西安电子科技大学学报,2014,41(3):162-168.
[11] 钱芳.认知OFDM网络资源分配技术研究[D].南京:南京理工大学,2016.
[12] 耿何晶.基于拍卖理论的虚拟机分配和定价机制研究[D].扬州:扬州大学,2017.
[2] Hsu Ching-Hsien,Wang Shang-guang,Zhang Yan,et al.Mobile edge computing[M].Beijing:Beijing University of Posts and Telecommunications Press,2017.
[3] Yu R,Ding J,Maharjan S,et al.Decentralized and optimal resource cooperation in geo-distributed mobile cloud computing[J].IEEE Transactions on Emerging Topics in Computing,2018,6(1):72-84.
[4] Li J,Zhu Y M,Hua Y Q,et al.Crowdsourcing sensing to smartphones:A randomized auction approach[C]//Proc of 2015 IEEE 23rd International Symposium on Quality of Service(IWQoS),2015:219-224.
[5] Pu L J,Chen X,Xu J D,et al.Crowd foraging:A QoS-oriented self-organized mobile crowdsourcing framework over opportunistic networks[J].IEEE Journal on Selected Areas in Communications,2017,35(4):848-862.
[6] Zhang H G,Liu B Y,Susanto H,et al.Incentive mechanism for proximity-based mobile crowed service systems[C]//Proc of the 35th Annual IEEE International Conference on Computer Communications,2016:1-9.
[7] Han Y Y,Wu H Y.Minimum-cost crowdsourcing with coverage guarantee in mobile opportunistic D2D networks[J].IEEE Transactions on Mobile Computing,2017,16(10):2806-2818.
[8] Xu Lei.Research on resource allocation for OFDM wireless network [D].Nanjing:Nanjing University of Aeronautics and Astronautics,2012.(in Chinese)
[9] Yu Tao,Zhou Xiao-tian.Virtual machine dynamic supply and allocation algorithm based on auction[J].Computer Engineering and Applications,2016,52(23):81-87.(in Chinese)
[10] Lu Di,Ma Jian-feng,Wang Yi-chuan,et al.Enhanced fairness-based multi-resource allocation algorithm for cloud computing[J].Journal of Xidian University,2014,41(3):162-168.
[11] Qian Fang.Research on resource allocation technology of cognitive OFDM network[D].Nanjing:Nanjing University of Science and Technology,2016.(in Chinese)
[12] Gen He-jing.Study on allocation of virtual machine instances and pricing mechanism based on auction theory [D].Yangzhou:Yangzhou University,2017.(in Chinese)
[1] 李孝斌,尹超.面向生产过程云服务的制造执行系统[J].计算机集成制造系统,2016,22(1):177-188.
[8] 徐雷.OFDM无线网络资源分配技术研究[D].南京:南京航空航天大学,2012.
[9] 于涛,周小天.基于拍卖的虚拟机动态供应和分配算法[J].计算机工程与应用,2016,52(23):81-87.
[10] 卢笛,马建峰,王一川,等.云计算下保障公平性的多资源分配算法[J].西安电子科技大学学报,2014,41(3):162-168.
[11] 钱芳.认知OFDM网络资源分配技术研究[D].南京:南京理工大学,2016.
[12] 耿何晶.基于拍卖理论的虚拟机分配和定价机制研究[D].扬州:扬州大学,2017.