Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/1879141.1879186acmconferencesArticle/Chapter ViewAbstractPublication PagesimcConference Proceedingsconference-collections
research-article

Revisiting the case for a minimalist approach for network flow monitoring

Published: 01 November 2010 Publication History

Abstract

Network management applications require accurate estimates of a wide range of flow-level traffic metrics. Given the inadequacy of current packet-sampling-based solutions, several application-specific monitoring algorithms have emerged. While these provide better accuracy for the specific applications they target, they increase router complexity and require vendors to commit to hardware primitives without knowing how useful they will be to meet the needs of future applications. In this paper, we show using trace-driven evaluations that such complexity and early commitment may not be necessary. We revisit the case for a "minimalist" approach in which a small number of simple yet generic router primitives collect flow-level data from which different traffic metrics can be estimated. We demonstrate the feasibility and promise of such a minimalist approach using flow sampling and sample-and-hold as sampling primitives and configuring these in a network-wide coordinated fashion using cSamp. We show that this proposal yields better accuracy across a collection of application-level metrics than dividing the same memory resources across metric-specific algorithms. Moreover, because a minimalist approach enables late binding to what application level metrics are important, it better insulates router implementations and deployments from changing monitoring needs.

References

[1]
Flexible Netflow. http: //www.cisco.com/en/US/products/ps6965/ products_ios_protocol_option_home.html,.
[2]
Juniper cflowd. http://www.juniper.net/ techpubs/software/junos/junos91/ swconfig-policy/cflowd.html.
[3]
Narus Intercept Solution. http://www.narus.com/ index.php/solutions/intercept.
[4]
NetFlow Input Filters. http://www.cisco.com/en/US/docs/ios/12_ 3t/12_3t4/feature/guide/gtnfinpf.html.
[5]
Packet Sampling, IETFWorking Group Charter. http://www.ietf.org/html.charters/psamp/ charter/.
[6]
K. Argyraki, P. Maniatis, O. Irzak, S. Ashish, and S. Shenker. Loss and Delay Accountability for the Internet. In Proc. ICNP, 2007.
[7]
D. Brauckhoff, B. Tellenbach, A. Wagner, A. Lakhina, and M. May. Impact of Traffic Sampling on Anomaly Detection Metrics. In Proc. IMC, 2006.
[8]
C. Cranor, T. Johnson, O. Spatscheck, and V. Shkapenyuk. Gigascope: A stream database for network applications. In Proc. ACM SIGMOD, 2003.
[9]
G. R. Cantieni, G. Iannaccone, C. Barakat, C. Diot, and P. Thiran. Reformulating the Monitor Placement problem: Optimal Network-Wide Sampling. In Proc. CoNeXT, 2006.
[10]
L. D. Carli, Y. Pan, A. Kumar, C. Estan, and K. Sankaralingam. PLUG: Flexible Lookup Modules for Rapid Deployment of New Protocols in High-speed Routers. In Proc. SIGCOMM, 2010.
[11]
B. Claise. Cisco Systems NetFlow Services Export Version 9. RFC 3954.
[12]
M. P. Collins and M. K. Reiter. Finding Peer-to-Peer File-sharing using Coarse Network Behaviors. In Proc. ESORICS, 2006.
[13]
G. Cormode and S. Muthukrishnan. An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms, 55, 2005.
[14]
N. Duffield, C. Lund, and M. Thorup. Charging from sampled network usage. In Proc. IMW, 2001.
[15]
N. Duffield, C. Lund, and M. Thorup. Estimating Flow Distributions from Sampled Flow Statistics. In Proc. of ACM SIGCOMM, 2003.
[16]
C. Estan, K. Keys, D. Moore, and G. Varghese. Building a Better NetFlow. In Proc. ACM SIGCOMM, 2004.
[17]
C. Estan and G. Varghese. New Directions in Traffic Measurement and Accounting. In Proc. ACM SIGCOMM, 2002.
[18]
A. Feldmann, A. G. Greenberg, C. Lund, N. Reingold, J. Rexford, and F. True. Deriving Traffic Demands for Operational IP Networks: Methodology and Experience. In Proc. ACM SIGCOMM, 2000.
[19]
Google sparse hash project. http: //code.google.com/p/google-sparsehash/.
[20]
H. Song, S. Dharmapurikar, J. Turner, and J. Lockwood. Fast Hash Table Lookup Using Extended Bloom Filter: An Aid to Network Processing. In Proc. ACM SIGCOMM, 2005.
[21]
N. Hohn and D. Veitch. Inverting Sampled Traffic. In Proc. IMC, 2003.
[22]
T. Karagiannis, D. Papagiannaki, and M. Faloutsos. BLINC: Multilevel Traffic Classification in the Dark. In Proc. ACM SIGCOMM, 2005.
[23]
K. Keys, D. Moore, and C. Estan. A Robust System for Accurate Real-time Summaries of Internet Traffic. In Proc. SIGMETRICS, 2005.
[24]
R. Kompella and C. Estan. The Power of Slicing in Internet Flow Measurement. In Proc. IMC, 2005.
[25]
B. Krishnamurthy, S. Sen, Y. Zhang, and Y. Chen. Sketch-based change detection: Methods, evaluation, and applications. In Proc. ACM IMC, 2003.
[26]
A. Kumar, M. Sung, J. Xu, and J. Wang. Data Streaming Algorithms for Efficient and Accurate Estimation of Flow Distribution. In Proc. ACM SIGMETRICS, 2004.
[27]
A. Kumar and J. Xu. Sketch Guided Sampling -- Using On-Line Estimates of Flow Size for Adaptive Data Collection. In Proc. IEEE Infocom, 2006.
[28]
A. Lakhina, M. Crovella, and C. Diot. Diagnosing Network-Wide Traffic Anomalies. In Proc. ACM SIGCOMM, 2004.
[29]
A. Lakhina, M. Crovella, and C. Diot. Mining anomalies using traffic feature distributions. In Proc. ACM SIGCOMM, 2005.
[30]
A. Lall, V. Sekar, J. Xu, M. Ogihara, and H. Zhang. Data Streaming Algorithms for Estimating Entropy of Network Traffic. In Proc. ACM SIGMETRICS, 2006.
[31]
Y. Lu, A. Montanari, B. Prabhakar, S. Dharmapurikar, and A. Kabbani. Counter Braids: A Novel Counter Architecture for Per-Flow Measurement. In Proc. SIGMETRICS, 2008.
[32]
M. P. Collins and M. K. Reiter. Hit-listWorm Detection and Bot Identification in Large Networks Using Protocol Graphs. In Proc. RAID, 2007.
[33]
H. Madhyastha and B. Krishnamurthy. A Generic Language for Application-Specific Flow Sampling. ACM CCR, 38(2):7--15, Apr. 2008.
[34]
J. Mai, C.-N. Chuah, A. Sridharan, T. Ye, and H. Zang. Is Sampled Data Sufficient for Anomaly Detection? In Proc. IMC, 2006.
[35]
A. Ramachandran, S. Seetharaman, and N. Feamster. Fast Monitoring of Traffic Subpopulations. In Proc. IMC, 2008.
[36]
B. Ribeiro, D. Towsley, T. Ye, and J. Bolot. Fisher information of sampled packets: an application to flow size estimation. In Proc. IMC, 2006.
[37]
S. Kumar and P. Crowley. Segmented Hash: An Efficient Hash Table Implementation for High Performance Networking Subsystems. In Proc. ACM ANCS, 2005.
[38]
V. Sekar, N. Duffield, K. van der Merwe, O. Spatscheck, and H. Zhang. LADS: Large-scale Automated DDoS Detection System. In Proc. USENIX ATC, 2006.
[39]
V. Sekar, M. K. Reiter,W. Willinger, H. Zhang, R. Kompella, and D. G. Andersen. cSamp: A System for Network-Wide Flow Monitoring. In Proc. NSDI, 2008.
[40]
V. Sekar, A. Gupta, M. K. Reiter and H. Zhang. Coordinated Sampling sans Origin-Destination Identifiers: Algorithms and Analysis. In Proc. COMSNSETS, 2010.
[41]
S. Venkataraman, D. Song, P. B. Gibbons, and A. Blum. New Streaming Algorithms for Fast Detection of Superspreaders . In Proc. NDSS, 2005.
[42]
Y. Xie, V. Sekar, D. A. Maltz, M. K. Reiter, and H. Zhang. Worm Origin Identification Using Random Moonwalks. In Proc. IEEE Symposium on Security and Privacy, 2005.
[43]
K. Xu, Z.-L. Zhang, and S. Bhattacharya. Profiling Internet Backbone Traffic: Behavior Models and Applications. In Proc. ACM SIGCOMM, 2005.
[44]
Y. Gao, Y. Zhao, R. Schweller, S. Venkataraman, Y. Chen, D. Song and M.-Y. Kao. Detecting Stealthy Attacks Using Online Histograms. In Proc. IWQoS, 2007.
[45]
L. Yuan, C.-N. Chuah, and P. Mohapatra. ProgME: Towards Programmable Network MEasurement. In Proc. SIGCOMM, 2007.
[46]
Q. Zhao, J. Xu, and Z. Liu. Design of a novel statistics counter architecture with optimal space and time efficiency. In Proc. ACM SIGMETRICS, 2006.

Cited By

View all
  • (2024) Marina : Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale IEEE Transactions on Network and Service Management10.1109/TNSM.2024.338239321:3(2773-2790)Online publication date: Jun-2024
  • (2024)Distributed Network Telemetry With Resource Efficiency and Full AccuracyIEEE/ACM Transactions on Networking10.1109/TNET.2023.332734532:3(1857-1872)Online publication date: Jun-2024
  • (2024)Survey on Predictive Algorithms to Detect Insider Threat on a Network Using Different Combination of Machine Learning Algorithms2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)10.1109/SEB4SDG60871.2024.10630366(1-14)Online publication date: 2-Apr-2024
  • Show More Cited By

Index Terms

  1. Revisiting the case for a minimalist approach for network flow monitoring

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      IMC '10: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
      November 2010
      496 pages
      ISBN:9781450304832
      DOI:10.1145/1879141
      • Program Chair:
      • Mark Allman
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      In-Cooperation

      • USENIX Assoc: USENIX Assoc

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 01 November 2010

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. anomaly detection
      2. data streaming
      3. sampling
      4. traffic monitoring

      Qualifiers

      • Research-article

      Conference

      IMC '10
      IMC '10: Internet Measurement Conference
      November 1 - 30, 2010
      Melbourne, Australia

      Acceptance Rates

      Overall Acceptance Rate 277 of 1,083 submissions, 26%

      Upcoming Conference

      IMC '24
      ACM Internet Measurement Conference
      November 4 - 6, 2024
      Madrid , AA , Spain

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)27
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 15 Oct 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (2024) Marina : Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale IEEE Transactions on Network and Service Management10.1109/TNSM.2024.338239321:3(2773-2790)Online publication date: Jun-2024
      • (2024)Distributed Network Telemetry With Resource Efficiency and Full AccuracyIEEE/ACM Transactions on Networking10.1109/TNET.2023.332734532:3(1857-1872)Online publication date: Jun-2024
      • (2024)Survey on Predictive Algorithms to Detect Insider Threat on a Network Using Different Combination of Machine Learning Algorithms2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)10.1109/SEB4SDG60871.2024.10630366(1-14)Online publication date: 2-Apr-2024
      • (2024) : Low-latency and reliable event collection in network measurement Journal of Network and Computer Applications10.1016/j.jnca.2024.103904228(103904)Online publication date: Aug-2024
      • (2024)Graph neural network based robust anomaly detection at service level in SDN driven microservice systemComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.110135239:COnline publication date: 1-Feb-2024
      • (2023)ChameleMon: Shifting Measurement Attention as Network State ChangesProceedings of the ACM SIGCOMM 2023 Conference10.1145/3603269.3604850(881-903)Online publication date: 10-Sep-2023
      • (2023)Eliminating Control Plane Overload via Measurement Task PlacementIEEE/ACM Transactions on Networking10.1109/TNET.2022.322342031:4(1717-1731)Online publication date: Aug-2023
      • (2023)Toward Low-Latency and Accurate State Synchronization for Programmable NetworksIEEE/ACM Transactions on Networking10.1109/TNET.2022.321844631:3(1400-1415)Online publication date: Jun-2023
      • (2022)Utilizing Deep Learning Techniques to Detect Zero Day Exploits in Network Traffic Flows2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON54665.2022.9965695(0163-0172)Online publication date: 26-Oct-2022
      • (2022)FlowShark: Sampling for High Flow Visibility in SDNsIEEE INFOCOM 2022 - IEEE Conference on Computer Communications10.1109/INFOCOM48880.2022.9796658(160-169)Online publication date: 2-May-2022
      • Show More Cited By

      View Options

      Get Access

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media