Dynamic Multi-Path Load Balancing Method Based on Feedforward Neural Network in DCN

doi:10.19678/j.issn.1000-3428.0059097

Abstract

Abstract: In order to solve the problem of unbalanced network load caused by elephant flows in Data Center Network(DCN), a dynamic multi-path load balancing method based on feedforward neural network(FNN) is proposed.In this method, topological perception and flow information monitoring are carried out first, and the elephant flows are marked.The collected network traffic information is then used as inputs to estimate the load of each link through the FNN.Finally, the optimal paths are found for the elephant flows by combining with the optimized ant colony algorithm, so that the elephant flows complete the paths selection according to the real-time state of the links.Simulation results show that the proposed method can effectively reduce network transmission delay and improve link utilization and network throughput.

Key words: Software Defined Network(SDN), Data Center Network(DCN), load balancing, Feedforward Neural Network(FNN), ant colony algorithm

摘要： 针对数据中心网络（DCN）中因大象流而引起的网络负载不均衡问题，提出一种基于前馈神经网络的动态多路径负载均衡方法。在拓扑感知和流量信息监控的基础上对大象流进行标记，将收集到的网络流量信息输入前馈神经网络以预估每段链路的负载，并结合优化蚁群算法为大象流寻找最优路径，使大象流根据链路的实时状态完成路径选择。仿真结果表明，该方法能够有效降低网络传输时延，提高链路利用率和网络吞吐量。

关键词: 软件定义网络, 数据中心网络, 负载均衡, 前馈神经网络, 蚁群算法

CLC Number:

TP393

ZUO Pan, SHU Yongan. Dynamic Multi-Path Load Balancing Method Based on Feedforward Neural Network in DCN[J]. Computer Engineering, 2021, 47(9): 113-119.

左攀, 束永安. DCN中基于前馈神经网络的动态多路径负载均衡方法[J]. 计算机工程, 2021, 47(9): 113-119.

/ Recommend / Download Citations

URL: https://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0059097

https://www.ecice06.com/EN/Y2021/V47/I9/113

Figures/Tables 8

References

[1] AL-FUQAHA A, GUIZANI M, MOHAMMADI M, et al.Internet of things:a survey on enabling technologies, protocols, and applications[J].IEEE Communications Surveys & Tutorials, 2015, 17(4):2347-2376.
[2] 张朝昆, 崔勇, 唐翯祎, 等.软件定义网络(SDN)研究进展[J].软件学报, 2015, 26(1):62-81. ZHANG C K, CUI Y, TANG H Y, et al.State-of-the-Art survey on Software-Defined Networking(SDN)[J].Journal of Software, 2015, 26(1):62-81.(in Chinese)
[3] AL-FARES M, LOUKISSAS A, VAHDAT A.A scalable, commodity data center network architecture[J].ACM SIGCOMM Computer Communication Review, 2009, 38(4):63-74.
[4] GREENBERG A, HAMILTON J R, JAIN N, et al.VL2:a scalable and flexible data center network[J].Communications of the ACM, 2011, 54(3):95-104.
[5] LEE B, KANAGAVELU R, AUNG K M M.An efficient flow cache algorithm with improved fairness in software-defined data center networks[C]//Proceedings of 2013 IEEE International Conference on Cloud Networking.Washington D.C., USA:IEEE Press, 2013:18-24.
[6] LIU F, GUO J, HUANG X, et al.eBA:efficient bandwidth guarantee under traffic variability in datacenters[J].IEEE ACM Transactions on Networking, 2017, 25(1):506-519.
[7] MCKEOWN N, ANDERSON T, BALAKRISHNAN H, et al.OpenFlow:enabling innovation in campus networks[J].ACM SIGCOMM Computer Communication Review, 2008, 38(2):69-74.
[8] DORIGO M, BIRATTARI M, STÜTZLE T.Ant colony optimization[J].IEEE Computational Intelligence Magazine, 2006, 1(4):28-39.
[9] HOPPS C.Analysis of an equal-cost multi-path algorithm:RFC2992[S].[S.l.]:RFC Editor, 2000.
[10] BALLANI H, COSTA P, GKANTSIDIS C, et al.Enabling end-host network functions[C]//Proceedings of 2015 ACM Conference on Special Interest Group on Data Communication.New York, USA:ACM Press, 2015:493-507.
[11] ALFARES 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, USA:USENIX, 2010:19.
[12] MAHAPATRA S, YUAN X.Load balancing mechanisms in data center networks[C]//Proceedings of the 7th International Conference and Expo on Emerging Technologies for a Smarter World.Incheon, Korea:CEWIT, 2010:1-5.
[13] HE K, ROZNER E, AGARWAL K, et al.Presto:edge-based load balancing for fast datacenter networks[C]//Proceedings of 2015 ACM Conference on Special Interest Group on Data Communication.New York, USA:ACM Press, 2015:465-478.
[14] CURTIS A R, KIM W H, YALAGANDULA P.Mahout:low-overhead datacenter traffic management using end-host-based elephant detection[C]//Proceedings of IEEE International Conference on Computer Communications.Washington D.C., USA:IEEE Press, 2011:1629-1637.
[15] PAKZAD F, PORTMANN M, TAN W L, et al.Efficient topology discovery in OpenFlow-based software defined networks[J].Computer Communications, 2016, 77:52-61.
[16] BALDI P, VERSHYNIN R.The capacity of feedforward neural networks[J].Neural Networks, 2019, 116:288-311.
[17] CAI G W, FANG Z, CHEN Y F.Estimating the number of hidden nodes of the single-hidden-layer feedforward neural networks[C]//Proceedings of the 15th International Conference on Computational Intelligence and Security.Washington D.C., USA:IEEE Press, 2019:172-176.
[18] 马振.改进蚁群算法及其在TSP中的应用研究[D].青岛:青岛理工大学, 2016. MA Z.Research on the improvement of ant colony algorithm and its application in TSP[D].Qingdao:Qingdao University of Technology, 2016.(in Chinese)
[19] Ryu OpenFlow controller[EB/OL].(2017-09-05)[2020-06-12].http://osrg.github.io/ryu/.
[20] Mininet:an instant virtual network on your laptop(or other PC)[EB/OL].(2018-08-28)[2020-06-12].http://mininet.org/.
[21] 张白, 宋安军.基于Iperf的网络性能测量研究[J].电脑知识与技术, 2009, 36(5):10227-10229. ZHANG B, SONG A J.Iperf-based network performance measurement[J].Computer Knowledge and Technology, 2009, 36(5):10227-10229.(in Chinese)

Please choose a citation manager

Content to export