Toward Advocacy-Free Evaluation of Packet Classification Algorithms

Understanding the real performance of a proposed algorithm is a basic requirement for both algorithm designers and implementers. However, this is sometimes difficult to achieve. Each new algorithm published is evaluated from different perspectives and based on different assumptions. Without a common ground, it is almost impossible to compare different algorithms directly. Choosing an incompetent algorithm for an application can incur significant cost. This is especially true for packet classification in network routers, since packet classification is intrinsically a hard problem and all existing algorithms are based on some heuristics and filter set characteristics. The performance of the packet classification subsystem is critical to the overall performance of the network routers. Although numerous algorithms have been proposed so far, a benchmark that can give them consistent evaluation and reveal their comparable performance is still missing. This paper summarizes our efforts toward improving this situation. First, we conduct a high-level survey on the existing algorithms and extract some insights on the general design ideas. Second, we describe an open-source platform dedicated for advocacy-free evaluation of packet classification algorithms. Many representative algorithms are actually implemented under a set of uniform conditions and assumptions. The freely available implementations allow other researchers to easily test them under different scenarios. We also enforce some consistent and fundamental criteria for the algorithm evaluation, so that their performance and potentials are directly comparable, regardless of the actual implementation platforms. This project serves dual purpose: It helps the researchers to accelerate the innovation in the area of packet classification algorithm development by relieving them from the labor of replicating the previous work and by enabling them to quickly compare and evaluate algorithms. Meanwhile, it also helps the system implementers to easily choose the capable algorithm for their particular applications. Aiming to build an open-source library, we encourage external contributions of new algorithm implementations and evaluations under the same framework. We believe the practice will benefit the research and design community as a whole.

[1]  V. Srinivasan,et al.  Fast address lookups using controlled prefix expansion , 1999, TOCS.

[2]  Haoyu Song,et al.  Shape shifting tries for faster IP route lookup , 2005, 13TH IEEE International Conference on Network Protocols (ICNP'05).

[3]  Randy H. Katz,et al.  Efficient multi-match packet classification with TCAM , 2004, Proceedings. 12th Annual IEEE Symposium on High Performance Interconnects.

[4]  Nick McKeown,et al.  Packet classification on multiple fields , 1999, SIGCOMM '99.

[5]  Venkatachary Srinivasan,et al.  Packet classification using tuple space search , 1999, SIGCOMM '99.

[6]  Thomas Y. C. Woo.  A modular approach to packet classification: algorithms and results , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[7]  Pankaj Gupta,et al.  Packet Classification using Hierarchical Intelligent Cuttings , 1999 .

[8]  Bin Liu,et al.  TCAM-based distributed parallel packet classification algorithm with range-matching solution , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[9]  George Varghese,et al.  Fast and scalable layer four switching , 1998, SIGCOMM '98.

[10]  T. V. Lakshman,et al.  SSA: a power and memory efficient scheme to multi-match packet classification , 2005, ANCS '05.

[11]  Haoyu Song,et al.  Packet classification using coarse-grained tuple spaces , 2006, 2006 Symposium on Architecture For Networking And Communications Systems.

[12]  T. V. Lakshman,et al.  High-speed policy-based packet forwarding using efficient multi-dimensional range matching , 1998, SIGCOMM '98.

[13]  Huan Liu,et al.  Efficient mapping of range classifier into ternary-CAM , 2002, Proceedings 10th Symposium on High Performance Interconnects.

[14]  Mark H. Overmars,et al.  Range Searching and Point Location among Fat Objects , 1996, J. Algorithms.

[15]  Antonius P. J. Engbersen,et al.  Fast and scalable packet classification , 2003, IEEE J. Sel. Areas Commun..

[16]  Bernhard Plattner,et al.  Scalable high speed IP routing lookups , 1997, SIGCOMM '97.

[17]  Haoyu Song,et al.  Efficient packet classification for network intrusion detection using FPGA , 2005, FPGA '05.

[18]  Francis Zane,et al.  Coolcams: power-efficient TCAMs for forwarding engines , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[19]  George Varghese,et al.  Packet classification using multidimensional cutting , 2003, SIGCOMM '03.

[20]  Jonathan S. Turner,et al.  Packet classification using extended TCAMs , 2003, 11th IEEE International Conference on Network Protocols, 2003. Proceedings..

[21]  George Varghese,et al.  Scalable packet classification , 2001, SIGCOMM '01.

[22]  George Varghese,et al.  Tree bitmap: hardware/software IP lookups with incremental updates , 2004, CCRV.

[23]  Anand Rangarajan,et al.  Algorithms for advanced packet classification with ternary CAMs , 2005, SIGCOMM '05.

[24]  David E. Taylor.  Survey and taxonomy of packet classification techniques , 2005, CSUR.

[25]  P. Gács,et al.  Algorithms , 1992 .

[26]  Jonathan S. Turner,et al.  Scalable packet classification using distributed crossproducing of field labels , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[27]  Anja Feldmann,et al.  Tradeoffs for packet classification , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[28]  Jonathan S. Turner,et al.  ClassBench: A Packet Classification Benchmark , 2005, IEEE/ACM Transactions on Networking.