Overview of Monitor Selection in Computer Networks

  • Nafei Zhu
  • Jiaping Zuo
  • Yue Zhou
  • Wei Wang
Part of the Communications in Computer and Information Science book series (CCIS, volume 320)


With the development of the computer networks, network measurement becomes more and more important and complicated. It may need to deploy many monitors and send many packets, which will introduce impact on the performance of the network. So, it is very meaningful to design network measurement architecture to get as much as the network information using as little as the measurement cost with sophisticated monitors selection, which is referred as the problem of monitor selection. The problem of monitor selection for flow and failures as well as delay is related in this paper. The technologies are dived into three categories which are based on the Graph Theory and the Mathematical Programming as well as the Group Betweenness Centrality theory. This division is coincident with the characteristics of the monitor selection technologies. Then, we go into the main research methods and results referring to each of the three categories. At last, we talk about the further research direction regarding the shortcomings of the present methods.


Monitor Selection Graph Theory Mathematical Programming Group Betweenness Centrality Theory 


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  1. 1.
    Kalidindi, S., Zekauskas, M.J.: Surveyor: An Infrastructure for Internet Performance Measurements. In: INET 1999, vol. 32, pp. 532–539. IEEE Communication Society (1999)Google Scholar
  2. 2.
    Paxson, V., Mahdavi, J., Adams, A., Mathis, M.: An Architecture for Large-scale Internet Measurement. IEEE Communications 36(8), 48–54 (2001)CrossRefGoogle Scholar
  3. 3.
    Claffy, K., Monk, T.E., McRobb, D.: Internet tomography. Nature, Web Matters (1999)Google Scholar
  4. 4.
    William, S.: SNMP, SNMPv2, SNMPv3, and RMON 1 and 2, 3rd edn., pp. 233–237. Addison Wesley (2009)Google Scholar
  5. 5.
    Cisco System: NetFlow services and application. Cisco System White Paper (1999) Google Scholar
  6. 6.
    Jacobsen, V.: Pathchar – A Tool to Infer Characteristics of Internet Paths (1997)Google Scholar
  7. 7.
    Cooperative Association for Internet Data Analysis (CAIDA),
  8. 8.
    Francis, P., Jamin, S., Jin, C., Jin, Y.: IDMaps: A global internet host distance estimation service. IEEE/ACM Trans. Networking 9(1), 525–540 (2001)CrossRefGoogle Scholar
  9. 9.
    Gummadi, K.P., Saroiu, S., Gribble, S.D.: King: Estimating Latency between Arbitrary Internet End Hosts. In: ACM IMW 2002, Marseille, France, vol. 36(2), pp. 346–351 (2002)Google Scholar
  10. 10.
    Zhang, B., Eugene Ng, T.S., Nandi, A., Riedi, R., Druschel, P., Wang, G.H.: Measurement-based Analysis, Modeling, and Synthesis of the Internet Delay Space. In: 6th Internet Measurement Conference (IMC), Rio de Janeiro, Brazil, vol. 29(4), pp. 85–98 (2006)Google Scholar
  11. 11.
    Sharma, P., Xu, Z.C., Banerjee, S., Lee, S.J.: Estimating network proximity and latency. In: Proceedings of the ACM SIGCOMM 2006, Pisa, Italy, vol. 13(7), pp. 41–50 (2006)Google Scholar
  12. 12.
    Agarwal, S., Lorch, J.R.: Match making for online games and other latency-sensitive P2P systems. In: ACM SIGCOMM 2009, Barcelona, Spain, vol. 7, pp. 677–682 (2009)Google Scholar
  13. 13.
    Borgatti, S.P., Everett, M.G.: A graph-theoretic perspective on centrality. Social Networks 28(4), 466–484 (2006)CrossRefGoogle Scholar
  14. 14.
    Jamin, S., Jin, C., Jin, Y., Raz, D., Shavitt, Y., Zhang, L.: On the placement of Internet instrumentation. In: IEEE INFOCOM 2000, vol. 35(8), pp. 295–304. IEEE Communication Society (2000)Google Scholar
  15. 15.
    Hochbaum, D.S.: Approximation Algorithm for NP-Hard Problems, pp. 231–233. PWS Publishing Company, Boston (1997)Google Scholar
  16. 16.
    Breitbart, Y., Chan, C.Y., Garofalakis, M., Rastogi, R., Silberschatz, A.: Efficiently monitoring bandwidth and latency in IP networks. In: EEE INFOCOM 2001, vol. 32(5), pp. 933–942. IEEE Communication Society (2001)Google Scholar
  17. 17.
    Horton, J., Lopez-Ortiz, A.: On the number of distributed measurement points for network tomography. In: ACM SIGCOMM IMC, vol. 27(3), pp. 204–209. ACM Press (2003)Google Scholar
  18. 18.
    Zang, H., Nucci, A.: Optimal NetFlow Deployment in IP Networks. In: 19th International Teletraffic Congress (ITC), Beijing, China, vol. 14(2), pp. 621–630 (2005)Google Scholar
  19. 19.
    Suh, K., Guo, Y., Kurose, J., Towsley, D.: Locating network monitors: Complexity, heuristics and coverage. In: IEEE INFOCOM, vol. 34(5), pp. 1564–1577 (March 2005)Google Scholar
  20. 20.
    Cantieni, G.R., Iannaccone, G., Barakat, C., Diot, C., Thiran, P.: Reformulating the Monitor Placement problem: Optimal Network-Wide Sampling. In: CoNeXT, vol. 37(8), pp. 312–318 (2006)Google Scholar
  21. 21.
    Chaudet, C., Fleury, E., Lassous, I., Hervé, Voge, M.E.: Optimal Positioning of Active and Passive Monitoring Devices. In: CoNeXT, vol. 23(4), pp. 93–124 (2005)Google Scholar
  22. 22.
    Dolev, S., Elovici, Y., Puzis, R., Zilberman, P.: Incremental Deployment of Network Monitors based on Group Betweenness Centrality. Information Processing Letters (2009)Google Scholar
  23. 23.
    Dolev, S., Elovici, Y., Puzis, R.: Routing Betweenness Centrality. Technical Report (2009)Google Scholar
  24. 24.
    Liu, X.H., Yin, J.P., Tang, L.L., Zhao, J.M.: A Monitoring Model for Link Bandwidth Usage of Network based on Weak Vertex Cover. Journal of Software 15(4), 545–549 (2004); (in Chinese with English abstract)zbMATHGoogle Scholar
  25. 25.
    Hu, C.C., Zhen, L.: On the Deployment Strategy of Distributed Network Security ensors. In: IEEE ICON, vol. 32(3), pp. 25–31. IEEE Press, Singapore (2005)Google Scholar
  26. 26.
    Zhang, J., Zhang, H., Wu, J.X.: Universal Model for Optimal Deployment of Network Flow Monitor. Journal of Chinese Computer Systems 14(6), 397–401 (2008); (in Chinese with English abstract)Google Scholar
  27. 27.
    Jang, H.Y., Li, W., Lin, Y.P., Zhang, Q.H.: A Distributed Algorithm for Monitor2Node s Selection in Net Traffic Measurement. Journal of Natural Science of Hunan Normal University 28(3), 1–21 (2006); (in Chinese with English abstract)Google Scholar
  28. 28.
    He, H., Hu, Z.M., Yun, C.X.: Network Latency Clustering for Detector Placementon Macroscopical Prewarning. Journal on Communications 2(1), 119–124 (2006); (in Chinese with English abstract)Google Scholar
  29. 29.
    Ge, H.W., Peng, Z.Y., Yue, H.B.: Hybrid Optimization Algorithm for Efficient Monitor Nodes Selection in Network Traffic. Application Research of Computers 4(9), 397–401 (2009); (in Chinese with English abstract)Google Scholar
  30. 30.
    Jackson, A.W., Milliken, W., Santivanez, C.A., Condell, M., Strayer, W.T.: A Topological Analysis of Monitor Placement Network Computing and Applications. In: Sixth IEEE International Symposium on Digital Object, vol. 56(5), pp. 323–328 (2007)Google Scholar
  31. 31.
    Hu, C.C., Liu, B., Liu, Z., Gao, S., Wu, D.O.: Optimal Deployment of Distributed Passive Measurement Monitors. In: ICC 2006, pp. 621–626 (2006)Google Scholar
  32. 32.
    Natu, M., Sethi: Probe Station Placement for Fault Diagnosis. In: IEEE GLOBECOM, pp. 125–129 (2007)Google Scholar
  33. 33.
    Agrawal, S., Naidu, K.V.M., Rastogi, R.: Diagnosing Link-level Anomalies Using Passive Probes. In: IEEE INFOCOM, pp. 465–470 (2007)Google Scholar
  34. 34.
    Cohen, R., Raz, D.: The Internet Dark Matter—On the Missing Links in the AS Connectivity Map. In: IEEE INFOCOM 2006, pp. 226–232. IEEE Communication Society (2006)Google Scholar
  35. 35.
    Nguyen, H.X., Thiran, P.: Active Measurement for Multiple Link Failures Diagnosis in IP Networks. In: Barakat, C., Pratt, I. (eds.) PAM 2004. LNCS, vol. 3015, pp. 185–194. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nafei Zhu
    • 1
  • Jiaping Zuo
    • 1
  • Yue Zhou
    • 1
  • Wei Wang
    • 1
  1. 1.National Application Software Testing LabsBeijingChina

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