Computer Science ›› 2017, Vol. 44 ›› Issue (7): 25-30.doi: 10.11896/j.issn.1002-137X.2017.07.005

Previous Articles     Next Articles

Method of Software Defined Network Path Abnormity Monitoring

LI Yang, CAI Zhi-ping and XIA Jing   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In the SDN architecture,the controller has to master the operating state of network data transmission path segment,which is quite significant for controller to implement the network security monitoring and traffic load balancing.Based on the characteristics of SDN architecture,the paper proposed an approach which combines reactive mea-surement and passive measurement for monitoring the network path segment abnormity.The method analyzes the opera-ting state of network traversing path from two aspects of path’s latency and available bandwidth,which can also perform the detection and give an alarm for the abnormal network path segments.Our experiments show that the method can not only measure the latency and available bandwidth of path segment,but also can detect the abnormal path segment out timely and send out alarm signals.In network conditions of constant and variable network cross traffic,the approach can monitor the abnormal path segment and measure the path’s performance parameters efficiently.

Key words: SDN,Latency measurement,Available bandwidth measurement,Path abnormity monitoring

[1] KREUTZ D,RAMOS F M V,VERSSIMO P E,et al.Software-Defined Networking:A Comprehensive Survey[J].Proceedings of the IEEE,2014,103(1):10-13.
[2] OpenFlow Networking Foundation.http://www.open-networking.org.
[3] YU C,LUMEZANU C,ZHANG Y,et al.FlowSense:Monito-ring Network Utilization with Zero Measurement Cost[C]∥International Conference on Passive and Active Measurement.2013:575-579.
[4] CHOWDHURY S R,BARI M F,AHMED R,et al.PayLess:A low cost network monitoring framework for Software Defined Networks[C]∥2014 IEEE/IFIP Network Operations and Mana-gement Symposium.2014:1-9.
[5] RAUMER D,SCHWAIGHOFER L,CARLE G.MonSamp:Adistributed SDN application for QoS monitoring[C]∥2014 Fede-rated Conference on Computer Science and Information Systems (FedCSIS).IEEE,2014:96 1-968.
[6] VAN ADRICHEM N L M,DOERR C,K UIPERS F A.OpenNetMon:Network monitoring in OpenFlow Software-Defined Networks[C]∥2014 IEEE/IFIP Network Operations and Ma-nagement Symposium.2014:1-8.
[7] BABIARZ J.A Two-Way Active Measurement Protocol (TW-AMP).http://tools.ietf.org/html/rfc5357.
[8] DONNELLY S,MCGREGOR T.Design principles for accurate passive measurement[M]∥Passive and Active Measurement Workshop,2000.
[9] Floodlight Controller.https://floodlight.atlassian.net/wiki.
[10] BOTTA A,DAINOTTI A,PESCAP A.A tool for the generation of realistic network workload for emerging networking scenarios[J].Computer Networks (Elsevier),2012,56(15):3531-3547.
[11] CAI Z P.Study on network measurement technology, model and algorithm based on active and passive measurement [D].Changsha:National University of Defense Technology,2005.(in Chinese) 蔡志平.基于主动和被动测量的网络测量技术、模型和算法研究[D].长沙:国防科学技术大学,2005.
[12] CHENG G,WANG Y X,HU Y F,et al.Network Link Fault Diagnosis Based on OpenFlow[J].Journal of Beijing University of Posts and Telecommunications,2015,8(5):42-46.(in Chinese) 程光,王玉祥,胡一非,等.基于OpenFlow的链路故障诊断方法[J].北京邮电大学学报,2015,38(5):42-46.
[13] CAI Z P,LIU F,ZHAO W T,et al.Network measurement deployment model and its optimization algorithm[J].Journal of Software,2008,9(2):419-431.(in Chinese) 蔡志平,刘芳,赵文涛,等.网络测量部署模型及其优化算法[J].软件学报,2008,19(2):419-431.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .