Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  百年期刊
Journal of Tsinghua University(Science and Technology)    2019, Vol. 59 Issue (2) : 148-153     DOI: 10.16511/j.cnki.qhdxxb.2018.25.049
COMPUTER SCIENCE AND TECHNOLOGY |
Low cost flow statistics collection in software defined networking
ZHAO Jun1, BAO Congxiao2, LI Xing1
1. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;(;
2. Information Technology Center, Tsinghua University, Beijing 100084, China
Download: PDF(1247 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  Many monitoring methods have been proposed for network measurements that are essential in software defined networking. However, periodically or adaptively collecting statistics from software switches using per-flow queries incurs significant communication costs thus increase the loads on switches. This paper presents an approach called OpenCost that decides which switch us used to collect statistics in software defined networks based on a non-linear integer programming (NLIP) model. However, the NLIP problem is NP-hard; therefore, the problem is solved using an approximation algorithm based on a greedy algorithm. Extensive simulations were used to benchmark the algorithm with the results showing that OpenCost reduces the communication costs by 55% on average compared with other methods.
Keywords software defined networking      network measurement      non-linear integer programming      OpenFlow     
Issue Date: 16 February 2019
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
ZHAO Jun
BAO Congxiao
LI Xing
Cite this article:   
ZHAO Jun,BAO Congxiao,LI Xing. Low cost flow statistics collection in software defined networking[J]. Journal of Tsinghua University(Science and Technology), 2019, 59(2): 148-153.
URL:  
http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.25.049     OR     http://jst.tsinghuajournals.com/EN/Y2019/V59/I2/148
  
  
  
  
  
  
  
  
[1] 赵俊, 包丛笑, 李星. 基于OpenFlow协议的覆盖网络路由器设计[J]. 清华大学学报(自然科学版), 2018, 58(2):164-169.ZHAO J, BAO C X, LI X. OpenFlow based software overlay router[J]. Journal of Tsinghua University (Science and Technology), 2018, 58(2):164-169. (in Chinese)
[2] YU C, LUMEZANU C, ZHANG Y P, et al. Flowsense:Monitoring network utilization with zero measurement cost[C]//Proceedings of the 14th International Conference on Passive and Active Network Measurement. Hong Kong, China:Springer, 2013:31-41.
[3] YU C, LUMEZANU C, SHARMA A, et al. Software-defined latency monitoring in data center networks[C]//Proceedings of the 16th International Conference on Passive and Active Network Measurement. New York, NY, USA:Springer, 2015:360-372.
[4] VAN ADRICHEM N L M, DOERR C, KUIPERS F A. OpenNetMon:Network monitoring in OpenFlow software-defined networks[C]//Proceedings of 2014 IEEE Network Operations and Management Symposium (NOMS). Krakow, Poland:IEEE, 2014:1-8.
[5] CHOWDHURY S R, BARI M F, AHMED R, et al. PayLess:A low cost network monitoring framework for software defined networks[C]//Proceedings of 2014 IEEE Network Operations and Management Symposium (NOMS). Krakow, Poland:IEEE, 2014:1-9.
[6] TOOTOONCHIAN A, GHOBADI M, GANJALI Y. OpenTM:Traffic matrix estimator for OpenFlow networks[C]//Proceedings of the 11th International Conference on Passive and Active Measurement. Zurich, Switzerland:Springer, 2010:201-210.
[7] The Numerical Algorithms Group. The NAG library for Python. (2018-05-09). https://www.nag.com/.
[8] CLEGG R, LANDA R, GRIFFIN D, et al. Faces in the clouds:Long-duration, multi-user, cloud-assisted video conferencing[J]. IEEE Transactions on Cloud Computing, 2017, doi:10.1109/TCC.2017.2680440.
[9] FIEDLER I, WILCKE A C. The market for online poker[R]. Rochester, NY, USA:SSNR, 2014:7-19.
[10] AMEIGEIRAS P, RAMOS-MUNOZ J J, NAVARRO-ORTIZ J, et al. Analysis and modelling of youtube traffic[J]. Transactions on Emerging Telecommunications Technologies, 2012, 23(4):360-377.
[11] GIOTSAS V, LUCKIE M, HUFFAKER B, et al. Inferring complex AS relationships[C]//Proceedings of the 2014 Conference on Internet Measurement Conference. Vancouver, BC, Canada:ACM, 2014.
[12] LANDA R, ARAÚ JO J T, CLEGG R G, et al. The large-scale geography of internet round trip times[C]//Proceedings of 2013 IFIP Networking Conference. Brooklyn, NY, USA:IEEE, 2013:1-9.
[1] LI Guo, HE Lin, SONG Guanglei, WANG Zhiliang, YANG Jiahai, LIN Jinlei, GAO Hao. IPv6 active address discovery algorithm based on multi-level classification and space modeling[J]. Journal of Tsinghua University(Science and Technology), 2021, 61(10): 1177-1185.
[2] ZHAO Jun, BAO Congxiao, LI Xing. OpenFlow based software overlay router[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(2): 164-169.
[3] Kun YU,Congxiao BAO,Xing LI. Internet path performance measurements using web servers[J]. Journal of Tsinghua University(Science and Technology), 2014, 54(4): 474-479.
[4] Zhongjin LIU,Yong LI,Li SU,Depeng JIN,Lieguang ZENG. TCAM-efficient flow table mapping scheme for OpenFlow multiple-table pipelines[J]. Journal of Tsinghua University(Science and Technology), 2014, 54(4): 437-442.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
Copyright © Journal of Tsinghua University(Science and Technology), All Rights Reserved.
Powered by Beijing Magtech Co. Ltd