基于软件定义网络的数据中心自适应多路径负载均衡算法

doi:10.11772/j.issn.1001-9081.2020060845

计算机应用 ›› 2021, Vol. 41 ›› Issue (4): 1160-1164.DOI: 10.11772/j.issn.1001-9081.2020060845

所属专题：网络与通信

基于软件定义网络的数据中心自适应多路径负载均衡算法

许红亮, 杨桂芹, 蒋占军

兰州交通大学电子与信息工程学院, 兰州 730070

收稿日期:2020-06-18 修回日期:2020-11-09 出版日期:2021-04-10 发布日期:2021-01-05
通讯作者: 杨桂芹
作者简介:许红亮（1996—），女，甘肃定西人，硕士研究生，主要研究方向：软件定义网络、负载均衡；杨桂芹（1970—），女，河南许昌人，教授，硕士，主要研究方向：现代信号处理理论、现代通信理论；蒋占军（1975—），男，宁夏中卫人，教授，博士，主要研究方向：移动通信。
基金资助:
甘肃省高原交通信息工程及控制重点实验室开放课题（20161106）；兰州交通大学“百名青年优秀人才培养计划”资助项目（150220232）。

Data center adaptive multi-path load balancing algorithm based on software defined network

XU Hongliang, YANG Guiqin, JIANG Zhanjun

School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou Gansu 730070, China

Received:2020-06-18 Revised:2020-11-09 Online:2021-04-10 Published:2021-01-05
Supported by:
This work is partially supported by the Gansu Provincial Open Project of the Key Laboratory of Plateau Traffic Information Engineering and Control (20161106), the Foundation of Hundred Excellent Young Talents Training Program of Lanzhou Jiaotong University (150220232).

摘要/Abstract

摘要： 针对传统多路径负载均衡算法无法有效地感知网络的运行状态、不能综合考虑链路的实时传输状态以及大多数算法缺少自适应性的问题，基于软件定义网络（SDN）的集中控制和全网管控思想，提出一种基于蜘蛛猴优化的SDN自适应多路径负载均衡算法（SMO-LBA）。首先，利用数据中心网络的感知能力来获取多路径的实时链路状态信息；然后，利用蜘蛛猴算法的全局探索和局部开采能力将链路空闲率作为每条路径的适应度值，并引入自适应权重对路径进行动态评估及更新；最后，寻找数据中心网络中链路占用率最小的路径，确定其为最优转发路径。选用胖树拓扑在Mininet平台上进行仿真实验，实验结果表明SMO-LBA可提高数据中心网络的吞吐量和平均链路利用率，实现网络自适应负载均衡。

关键词: 软件定义网络, 多路径负载均衡, 蜘蛛猴优化算法, 胖树, Mininet

Abstract: The traditional multi-path load balancing algorithms cannot effectively perceive the running state of the network, cannot comprehensively consider the real-time transmission states of the links and most of them lack adaptability. In order to solve these problems, a Software Defined Network(SDN) adaptive multi-path Load Balancing Algorithm based on Spider Monkey Optimization(SMO-LBA) was proposed based on the idea of centralized control and whole network control of SDN. Firstly, the perceptul ability of data center network was used to obtain the multi-path real-time link state information. Then, based on the global exploration and local exploitation ability of spider monkey optimization algorithm, the link idle rate was used as the adaptability value of each path, and the paths were dynamically evaluated and updated by introducing the adaptive weight. Finally, the path with the lowest link occupancy rate in data center network was determined as the optimal forwarding path. The fat tree topology was selected to carry out the simulation experiment on Mininet platform. Experimental results show that SMO-LBA can improve the throughput and average link utilization of data center network, and realize the adaptive load balancing of the network.

Key words: Software Defined Network (SDN), multi-path load balancing, Spider Monkey Optimization (SMO) algorithm, fat tree, Mininet

中图分类号:

TP393

许红亮, 杨桂芹, 蒋占军. 基于软件定义网络的数据中心自适应多路径负载均衡算法[J]. 计算机应用, 2021, 41(4): 1160-1164.

XU Hongliang, YANG Guiqin, JIANG Zhanjun. Data center adaptive multi-path load balancing algorithm based on software defined network[J]. Journal of Computer Applications, 2021, 41(4): 1160-1164.

参考文献

[1] ASKAR S. SDN-based load balancing scheme for fat-tree data center networks[J]. Al-Nahrain Journal for Engineering Sciences, 2017,20(5):1047-1056.
[2] SUN Z,LI J,YANG K. Software-defined networking[J]. ZTE Communications,2014,12(2):1-2.
[3] 董仕, 李瑞轩, 李晓林. 基于软件定义数据中心网络的节能路由算法[J]. 计算机研究与发展,2015,52(4):806-812.(DONG S, LI R X,LI X L. Energy efficient routing algorithm based on software defined data center network[J]. Journal of Computer Research and Development,2015,52(4):806-812.)
[4] JAMALI S,BADIRZADEH A,SIAPOUSH M S. On the use of the genetic programming for balanced load distribution in softwaredefined networks[J]. Digital Communications and Networks, 2019,5(4):288-296.
[5] CHEMERITSKIY E,SMELANSKY R. On QoS management in SDN by multipath routing[C]//Proceedings of the 2014 International Science and Technology Conference (Modern Networking Technologies). Piscataway:IEEE,2014:1-6.
[6] 金勇, 刘亦星, 王欣欣. 基于SDN的数据中心网络多路径流量调度算法[J]. 计算机科学,2019,46(6):90-94.(JIN Y,LIU Y X, WANG X X. SDN-based multipath traffic scheduling algorithm for data center network[J]. Computer Science,2019,46(6):90-94.)
[7] AL-FARES M,RADHAKRISHNAN S,RAGHAVAN B,et al. Hedera:dynamic flow scheduling for data-center networks[C]//Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation. Berkeley:USENIX Association, 2010:1-15.
[8] LI Y,PAN D. OpenFlow based load balancing for fat-tree networks with multipath support[C]//Proceedings of the 2013 IEEE International Conference on Communications. Piscataway:IEEE, 2013:8-21.
[9] LU Y,FU B,XI X,et al. An SDN-based flow control mechanism for guaranteeing QoS and maximizing throughput[J]. Wireless Personal Communications,2017,97(1):417-442.
[10] 孙三山, 汪帅, 樊自甫. 软件定义网络架构下基于流调度代价的数据中心网络拥塞控制路由算法[J]. 计算机应用,2016,36(7):1784-1788. (SUN S S, WANG S, FAN Z F. Flow scheduling cost based congestion control routing algorithm for data center network on software defined network architecture[J]. Journal of Computer Applications,2016,36(7):1784-1788.)
[11] 何东泽, 杨桂芹. 基于SDN的数据中心多路径负载均衡算法[J]. 兰州交通大学学报,2019,38(4):40-46.(HE D Z,YANG G Q. Multipath load balancing algorithm based on SDN center[J]. Journal of Lanzhou Jiaotong University,2019,38(4):40-46.)
[12] 鲍新春. 基于SDN/OpenFlow的数据中心网络的研究[D]. 成都:电子科技大学,2016:6-9.(BAO X C. Research on data center network based on SDN/OpenFlow[D]. Chengdu:University of Electronic Science and Technology of China,2016:6-9.)
[13] 党婷婷, 林丹. 含有动态自适应惯性权重的蜘蛛猴优化算法[J]. 计算机工程与应用,2019,55(14):40-47.(DANG T T, LIN D. Spider monkey optimization algorithm with dynamic selfadaptive inertia weight[J]. Computer Engineering and Applications,2019,55(14):40-47.)
[14] 王吉权, 王福林. 萤火虫算法的改进分析及应用[J]. 计算机应用,2014,34(9):2552-2556. (WANG J Q,WANG F L. Improvement analysis and application of firefly algorithm[J]. Journal of Computer Applications,2014,34(9):2552-2556.)
[15] NORING U. Investigating the possibility of speeding up Mininet by using Netmap,an alternative Linux packet I/O framework[J]. Procedia Computer Science,2018,126:1885-1894.
[16] JO E,PAN D,LIU J,et al. A simulation and emulation study of SDN-based multipath routing for fat-tree data center networks[C]//Proceedings of the 2014 Winter Simulation Conference. Piscataway:IEEE,2014:3072-3083.
[17] ZHU L,RAN Y,SUN L,et al. Dynamic multi-tree switching for multimedia multicast in an OpenFlow-based fat-tree network[C]//Proceedings of the 2017 International Forum on Digital TV and Wireless Multimedia Communications, CCIS 815. Singapore:Springer,2017:392-406.

基于软件定义网络的数据中心自适应多路径负载均衡算法

Data center adaptive multi-path load balancing algorithm based on software defined network

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	朱梦迪, 束永安. 软件定义网络中控制数据平面一致性的验证[J]. 计算机应用, 2020, 40(6): 1751-1754.
[2]	向雄, 田检. 基于软件定义网络的对等网传输调度优化[J]. 计算机应用, 2020, 40(3): 777-782.
[3]	赵季红, 吴豆豆, 曲桦, 殷振宇. 基于软件定义网络的可靠性虚拟网络映射保障机制[J]. 计算机应用, 2020, 40(3): 770-776.
[4]	池亚平, 莫崇维, 杨垠坦, 陈纯霞. 面向软件定义网络架构的入侵检测模型设计与实现[J]. 计算机应用, 2020, 40(1): 116-122.
[5]	贾梦瑶, 王兴伟, 张爽, 易波, 黄敏. 基于软件定义网络的卫星网络容错路由机制[J]. 计算机应用, 2019, 39(6): 1772-1779.
[6]	李兆斌, 刘泽一, 魏占祯, 韩禹. 基于哈希链的软件定义网络路径安全[J]. 计算机应用, 2019, 39(5): 1368-1373.
[7]	邹承明, 刘攀文, 唐星. 动态主机配置协议泛洪攻击在软件定义网络中的实时防御[J]. 计算机应用, 2019, 39(4): 1066-1072.
[8]	范宏伟, 胡宇翔, 兰巨龙. 两段式虚拟网络功能硬件加速资源部署机制[J]. 计算机应用, 2018, 38(9): 2575-2580.
[9]	朱晓东, 王劲林, 王玲芳. 软件定义网络中协同存储数据面快速转发[J]. 计算机应用, 2018, 38(8): 2343-2347.
[10]	李兆斌, 李伟隆, 魏占祯, 刘梦甜. SDN数据安全处理机制关键模块的研究与实现[J]. 计算机应用, 2018, 38(7): 1929-1935.
[11]	徐佳庆, 万文, 蔡东京, 唐付桥, 何杰, 张磊. 高维胖树系统中确定性路由容错策略实现[J]. 计算机应用, 2018, 38(5): 1393-1398.
[12]	樊自甫, 周凯恒, 姚杰. 基于博弈论的SDN主控制器重选机制[J]. 计算机应用, 2018, 38(3): 776-779.
[13]	张传浩, 周桥. 节点效用最大化的服务功能链构建方法[J]. 计算机应用, 2018, 38(2): 503-508.
[14]	张传浩, 谷学汇, 孟彩霞. 基于软件定义网络的反嗅探攻击方法[J]. 计算机应用, 2018, 38(11): 3258-3262.
[15]	吴诗奎, 王艳. 软件定义网络的异构无线网络资源分配框架[J]. 计算机应用, 2018, 38(11): 3293-3298.