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.
- M. Allman, V. Paxson, and W. Stevens. TCP congestion control. IETF Network Working Group RFC 2581, 1999.]] Google ScholarDigital Library
- 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.]] Google ScholarDigital Library
- E. Altman, K. Avrachenkov, and C. Barakat. A stochastic model of TCP/IP with stationary random losses. In SIGCOMM'2000, 2000.]] Google ScholarDigital Library
- P. Barford and M. Crovella. Generating Representative Web Workloads for Network and Server Performance Evaluation. In Proceedings of ACM Sigmetrics, June 1998.]] Google ScholarDigital Library
- 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.]] Google ScholarDigital Library
- P. Barford and D. Plonka. Characteristics of Network Traffic Flow Anomalies. In Proceedings of ACM SIGCOMM Internet Measurement Workshop, Oct 2001.]] Google ScholarDigital Library
- 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.]]Google Scholar
- 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.]] Google ScholarDigital Library
- 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.]] Google ScholarDigital Library
- K. Claffy. Internet traffic characterization. PhD thesis, UC San Diego, 1994.]] Google ScholarDigital Library
- C. Cranor, T. Johnson, and O. Spatscheck. Gigascope: a stream database for network applications. In SIGMOD, June 2003.]] Google ScholarDigital Library
- C. Dewes, A. Wichmann, and A. Feldmann. An analysis of Internet chat systems. In Proceedings of ACM SIGCOMM Internet Measurement Conference, Oct 2003.]] Google ScholarDigital Library
- C. Estan, S. Savage, and G. Varghese. Automatically inferring patterns of resource consumption in network traffic. In ACM SIGCOMM, Karlsruhe, Germany, 2003.]] Google ScholarDigital Library
- 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.]] Google ScholarDigital Library
- 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.]] Google ScholarDigital Library
- S. Floyd. Connections with multiple congested gateways in packet-switched networks, part I: One way traffic. Computer Communications Review, 21(5), 1991.]] Google ScholarDigital Library
- 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.]]Google Scholar
- 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.]] Google ScholarDigital Library
- T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001.]]Google Scholar
- IANA. Internet Assigned Numbers Authority (IANA). http://www.iana.org/assignments/port-numbers.]]Google Scholar
- V. Jacobson. Congestion avoidance and control. Communication Review, 18(4):314--329, 1988. Available at ftp://ftp.ee.lbl.gov/papers/congavoid.ps.Z.]] Google ScholarDigital Library
- B. Krishnamurthy and J. Rexford. Web Protocols and Practice, chapter Chapter 10: Web Workload Characterization. Addison-Wesley, 2001.]]Google Scholar
- 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.]]Google Scholar
- 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.]] Google ScholarDigital Library
- 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.]]Google Scholar
- 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.]] Google ScholarDigital Library
- White paper-netflow services and applications. http://www.cisco.com/warp/public/cc/pd/iosw/ioft/neflct/tech/napps_ wp.htm.]]Google Scholar
- 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.]] Google ScholarDigital Library
- V. Paxson. Empirically derived analytic models of wide-area TCP connections. IEEE/ACM Transactions on Networking, 2(4):316--336, 1994.]] Google ScholarDigital Library
- V. Paxson and S. Floyd. Wide-area traffic: The failure of Poisson modeling. IEEE/ACM Transactions on Networking, 3(3):226--244, June 1995.]] Google ScholarDigital Library
- J. E. Pitkow. Summary of WWW characterizations. W3J, 2:3--13, 1999.]] Google ScholarDigital Library
- 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.]] Google ScholarDigital Library
- J. Tukey and P. Tukey. Strips displaying empirical distributions: I. textured dot strips. Technical report, Bellcore Technical Memorandum, 1990.]]Google Scholar
- 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.]]Google Scholar
- 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.]] Google ScholarDigital Library
- Y. Zhang and V. Paxson. Detecting backdoors. In Proc. USENIX, Denver, Colorado, USA, 2000.]] Google ScholarDigital Library
Index Terms
- Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification
Recommendations
A PID-based algorithm to guarantee QoS delay requirements in LR-PONs
In this paper a novel algorithm with delay guarantees for high priority traffic based on a Proportional (P) controller for Long-Reach Passive Optical Networks (LR-PONs) is proposed. We have recently demonstrated that Proportional-Integral-Derivative (...
A dynamic multiple-threshold bandwidth reservation (DMTBR) scheme for QoS provisioning in multimedia wireless networks
Next-generation wireless networks target to provide quality of service (QoS) for multimedia applications. We study the wireless systems that support two QoS requirements: keeping the handoff dropping probability less than a predefined QoS threshold ...
Provisioning QoS guarantee by multipath routing and reservation in Ad hoc networks
AbstractIn this paper, a QoS multipath source routing protocol (QoS-MSR) is proposed for ad hoc networks. It can collect QoS information through route discovery mechanism of multipath source routing (MSR) and establish QoS route with reserved bandwidth. ...
Comments