Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/1028788.1028805acmconferencesArticle/Chapter ViewAbstractPublication PagesimcConference Proceedingsconference-collections
Article

Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification

Published: 25 October 2004 Publication History

Abstract

The ability to provide different Quality of Service (QoS) guarantees to traffic from different applications is a highly desired feature for many IP network operators, particularly for enterprise networks. Although various mechanisms exist for providing QoS in the network, QoS is yet to be widely deployed. We believe that a key factor holding back widespread QoS adoption is the absence of suitable methodologies/processes for appropriately mapping the traffic from different applications to different QoS classes. This is a challenging task, because many enterprise network operators who are interested in QoS do not know all the applications running on their network, and furthermore, over recent years port-based application classification has become problematic. We argue that measurement based automated Class of Service (CoS) mapping is an important practical problem that needs to be studied.
In this paper we describe the requirements and associated challenges, and outline a solution framework for measurement based classification of traffic for QoS based on statistical application signatures. In our approach the signatures are chosen in such as way as to make them insensitive to the particular application layer protocol, but rather to determine the way in which an application is used -- for instance is it used interactively, or for bulk-data transport. The resulting application signature can then be used to derive the network layer signatures required to determine the CoS class for individual IP datagrams. Our evaluations using traffic traces from a variety of network locations, demonstrate the feasibility and potential of the approach.

References

[1]
M. Allman, V. Paxson, and W. Stevens. TCP congestion control. IETF Network Working Group RFC 2581, 1999.]]
[2]
J. Almeida, J. Krueger, D. Eager, and M. Vernon. Analysis of educational media server workloads. In Proc. Inter. Workshop on Network and Operating System Support for Digital Audio and Video, June 2001.]]
[3]
E. Altman, K. Avrachenkov, and C. Barakat. A stochastic model of TCP/IP with stationary random losses. In SIGCOMM'2000, 2000.]]
[4]
P. Barford and M. Crovella. Generating Representative Web Workloads for Network and Server Performance Evaluation. In Proceedings of ACM Sigmetrics, June 1998.]]
[5]
P. Barford, J. Kline, D. Plonka, and A. Ron. A Signal Analysis of Network Traffic Anomalies. In Proceedings of ACM SIGCOMM Internet Measurement Workshop, Nov 2002.]]
[6]
P. Barford and D. Plonka. Characteristics of Network Traffic Flow Anomalies. In Proceedings of ACM SIGCOMM Internet Measurement Workshop, Oct 2001.]]
[7]
Y. Bernet, J. Binder, S. Blake, M. Carlson, B. Carpenter, S. Keshav, E. Davies, B. Ohlman, Z. Wang, and W. Weiss. A framework for differentiated services. Internet Draft, February 1999. http://search.ietf.org/internet-drafts/draft-ietf-diffserv-framework-02.txt.]]
[8]
S. Blake, D. Black, D. Black, H. Schulzrinne, D. Black, M. Carlson, E. Davies, Z. Wang, and W. Weiss. Rfc 2475 - an architecture for differentiated service, December 1998. Available at http://www.faqs.org/rfcs/rfc2475.html.]]
[9]
M. Chesire, A. Wolman, G. M. Voelker, and H. M. Levy. Measurement and analysis of a streaming media workload. In Proc. USENIX Symposium on Internet Technologies and Systems, March 2001.]]
[10]
K. Claffy. Internet traffic characterization. PhD thesis, UC San Diego, 1994.]]
[11]
C. Cranor, T. Johnson, and O. Spatscheck. Gigascope: a stream database for network applications. In SIGMOD, June 2003.]]
[12]
C. Dewes, A. Wichmann, and A. Feldmann. An analysis of Internet chat systems. In Proceedings of ACM SIGCOMM Internet Measurement Conference, Oct 2003.]]
[13]
C. Estan, S. Savage, and G. Varghese. Automatically inferring patterns of resource consumption in network traffic. In ACM SIGCOMM, Karlsruhe, Germany, 2003.]]
[14]
K. Fall and S. Floyd. Simulation-based comparisons of Tahoe, Reno, and SACK TCP. Computer Communication Review, 26(3):5--21, 1996. Available at http://www.aciri.org/floyd/papers.html.]]
[15]
A. Feldmann, A. Gilbert, and W. Willinger. Data networks as cascades: Explaining the multifractal nature of internet WAN traffic. In Proceedings of the ACM Sigcomm'98, Vancouver, Canada, 1998.]]
[16]
S. Floyd. Connections with multiple congested gateways in packet-switched networks, part I: One way traffic. Computer Communications Review, 21(5), 1991.]]
[17]
C. Gbaguidi, H. Einsiedler, P. Hurley, W. Almesberger, and J. P. Hubaux. A survey of differentiated services architectures for the Internet, March 1998. http://sscwww.epfl.ch/Pages/publications/ps_files/tr98_020.ps.]]
[18]
A. C. Gilbert, S. Guha, P. Indyk, Y. Kotidis, S. Muthukrishnan, and M. J. Strauss. Fast, small-space algorithms for approximate histogram maintenance. In STOC, 2002.]]
[19]
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001.]]
[20]
IANA. Internet Assigned Numbers Authority (IANA). http://www.iana.org/assignments/port-numbers.]]
[21]
V. Jacobson. Congestion avoidance and control. Communication Review, 18(4):314--329, 1988. Available at ftp://ftp.ee.lbl.gov/papers/congavoid.ps.Z.]]
[22]
B. Krishnamurthy and J. Rexford. Web Protocols and Practice, chapter Chapter 10: Web Workload Characterization. Addison-Wesley, 2001.]]
[23]
Z. Liu, M. S. Squillante, C. H. Xia, S. zheng Yu, and L. Zhang. Profile-based traffic chacterization of commercial web sites. In J.Charzinski, R.Lehnert, and P.Tan-Gia, editors, Proceedings of the 18th International Teletraffic Congress (ITC-18), volume 5a, pages 231--240, Berlin, Germany, 2003.]]
[24]
M. Mathis, J. Semke, J. Mahdavi, and T. Ott. The macroscopic behavior of the TCP congestion avoidance algorithm. Computer Communication Review, 27(3):67--82, July 1997. Available at http://www.psc.edu/networking/tcp_friendly.html#performance.]]
[25]
J. Micheel, I. Graham, and N. Brownlee. The A uckland data set: an access link observed. In the 14th ITC Specialist Seminar on Access Networks and Systems, Barcelona, Spain, April 25th-27th 2001.]]
[26]
D. Moore, G. Voelker, and S. Savage. Inferring Internet Denial of Service Activity. In Proc. of the USENIX Security Symposium, Washington D.C., August 2001. Available at http://www.cs.ucsd.edu/ savage/papers/UsenixSec01.pdf.]]
[27]
White paper-netflow services and applications. http://www.cisco.com/warp/public/cc/pd/iosw/ioft/neflct/tech/napps_ wp.htm.]]
[28]
J. Padhye, V. Firoin, D. Towsley, and J. Kurose. Modeling TCP throughput: A simple model and its empirical validation. In ACM SIGCOMM'98, 1998. Available at http://www.psc.edu/networking/tcp_friendly.html#performance.]]
[29]
V. Paxson. Empirically derived analytic models of wide-area TCP connections. IEEE/ACM Transactions on Networking, 2(4):316--336, 1994.]]
[30]
V. Paxson and S. Floyd. Wide-area traffic: The failure of Poisson modeling. IEEE/ACM Transactions on Networking, 3(3):226--244, June 1995.]]
[31]
J. E. Pitkow. Summary of WWW characterizations. W3J, 2:3--13, 1999.]]
[32]
H. Schulzrinne, A. Rao, and R. Lanphier. Real time streaming protocol (RTSP), request for comments 2326, April 1998. ftp://ftp.isi.edu/in-notes/rfc2326.]]
[33]
J. Tukey and P. Tukey. Strips displaying empirical distributions: I. textured dot strips. Technical report, Bellcore Technical Memorandum, 1990.]]
[34]
J. van der Merwe, S. Sen, and C. Kalmanek. Streaming video traffic: Characterization and network impact. In 7th International Web Content Caching and Distribution workshop (WCW), Boulder, Colorado, August 14th-16th 2002.]]
[35]
W. Willinger, M. S. Taqqu, R. Sherman, and D. V. Wilson. Self-similarity through high-variability: Statistical analysis of Ethernet LAN traffic at the source level. Proceedings of the ACM SIGCOMM'95, 1995. Available at http://www.acm.org/sigcomm/sigcomm95/sigcpapers.html.]]
[36]
Y. Zhang and V. Paxson. Detecting backdoors. In Proc. USENIX, Denver, Colorado, USA, 2000.]]

Cited By

View all

Index Terms

  1. Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      IMC '04: Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
      October 2004
      386 pages
      ISBN:1581138210
      DOI:10.1145/1028788
      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

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 25 October 2004

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. class of service (CoS)
      2. quality of service (QoS)
      3. statistical signature
      4. traffic classification

      Qualifiers

      • Article

      Conference

      IMC04
      Sponsor:
      IMC04: Internet Measurement Conference
      October 25 - 27, 2004
      Taormina, Sicily, Italy

      Acceptance Rates

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

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)136
      • Downloads (Last 6 weeks)11
      Reflects downloads up to 13 Nov 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)HClassJournal of High Speed Networks10.3233/JHS-23014530:4(517-533)Online publication date: 15-Oct-2024
      • (2024)Knowledge-distillation-based Traffic Classification for Space-Ground-Integrated Network2024 43rd Chinese Control Conference (CCC)10.23919/CCC63176.2024.10662183(6127-6132)Online publication date: 28-Jul-2024
      • (2024)AN-Net: an Anti-Noise Network for Anonymous Traffic ClassificationProceedings of the ACM Web Conference 202410.1145/3589334.3645691(4417-4428)Online publication date: 13-May-2024
      • (2024)Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty QuantificationIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32441685:1(420-433)Online publication date: Jan-2024
      • (2024)Network Traffic Classification with Small-Scale Datasets Using Ensemble LearningICC 2024 - IEEE International Conference on Communications10.1109/ICC51166.2024.10623093(1-6)Online publication date: 9-Jun-2024
      • (2024)In-Network Machine Learning Using Programmable Network Devices: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334435126:2(1171-1200)Online publication date: Oct-2025
      • (2024)An adaptive classification and updating method for unknown network traffic in open environmentsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.110114238:COnline publication date: 1-Jan-2024
      • (2024)Incremental federated learning for traffic flow classification in heterogeneous data scenariosNeural Computing and Applications10.1007/s00521-024-10281-436:32(20401-20424)Online publication date: 12-Aug-2024
      • (2023)The Current Research Status of AI-Based Network Security Situational AwarenessElectronics10.3390/electronics1210230912:10(2309)Online publication date: 19-May-2023
      • (2023)A Multimodal Network Security Framework for Healthcare Based on Deep LearningComputational Intelligence and Neuroscience10.1155/2023/90413552023:1Online publication date: 20-Feb-2023
      • 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