Stateful hardware decompression in networking environment

ABSTRACT

Compression and Decompression can significantly lower the network bandwidth requirements for common internet traffic. Driven by the demands of an enterprise network intrusion system, this paper defines and examines the requirements of popular dictionary-based decompression in the real-time network processing scenario. In particular, a "stateful" decompression is required that arises out of the packet oriented nature of current networks, where the decompression of the data of a packet depends on the decompressed contents of its preceeding packets composing the same data stream. We propose an effective hardware decompression acceleration engine, which fetches the history data into the accelerator's fast memory on-demand and hides the associated latency by exploring the parallelism of the dictionary-based decompression process. We specify and evaluate various design and implementation options of the fetch-on-demand mechanism, i.e. prefetch most frequently used history, on-accelerator history buffer management, and reuse of fetched history data. Through simulation-based performance study, we show the effectiveness of the proposed mechanism on hiding the overhead of stateful decompression. We further show the effects of the design options and the impact on the overall performance of the network service stack of an intrusion prevension system.

REFERENCES

• 1: Ross Arnold , Tim Bell, A Corpus for the Evaluation of Lossless Compression Algorithms, Proceedings of the Conference on Data Compression, p.201, March 25-27, 1997
• 2: Comtech AHA Corporation. AHA362-PCIX GZIP Compression Accelerator Quick Start Guide. http://www.aha.com/show_pub.php?id=200, 2008.
• 3: P. Deutsch, DEFLATE Compressed Data Format Specification version 1.3, RFC Editor, 1996
• 4: S. M. Dorward and S. Quinlan. Method and apparatus for data compression of network packets. U. S. Patent 6,388,584, 2002.
• 5: P. Franaszek , J. Robinson , J. Thomas, Parallel compression with cooperative dictionary construction, Proceedings of the Conference on Data Compression, p.200, March 31-April 03, 1996
• 6: IBM. Core Connect Bus Architecture. IBM White Paper at http://www.ibm.com/chips/techlib/techlib.nsf/literature/CoreConnect_Bus_ Architecture, 1999.
• 7: IBM Internet Security Systems (ISS). Proventia Network IPS GX6116. http://www.iss.net/products/index.html, 2008.
• 8: Indra Networks. SC MX4E Compression Card. http://www.indranetworks.com/SCMX4E.html, 2008.
• 9: Shmuel Tomi Klein , Yair Wiseman, Parallel Lempel Ziv coding, Discrete Applied Mathematics, v.146 n.2, p.180-191, 1 March 2005 [doi>10.1016/j.dam.2004.04.013]
• 10: Piti Piyachon , Yan Luo, Efficient memory utilization on network processors for deep packet inspection, Proceedings of the 2006 ACM/IEEE symposium on Architecture for networking and communications systems, December 03-05, 2006, San Jose, California, USA [doi>10.1145/1185347.1185358]
• 11: Port80 Software. Port80 Surveys HTTP Compression on the Top 100 Corporations' Web Sites. http://www.port80software.com/surveys/top1000compression/, 2007.
• 12: H. Shafi , P. J. Bohrer , J. Phelan , C. A. Rusu , J. L. Peterson, Design and validation of a performance and power simulator for PowerPC systems, IBM Journal of Research and Development, v.47 n.5-6, p.641-651, September 2003
• 13: B. Sinharoy , R. N. Kalla , J. M. Tendler , R. J. Eickemeyer , J. B. Joyner, POWER5 System microarchitecture, IBM Journal of Research and Development, v.49 n.4/5, p.505-521, July 2005
• 14: M. E. Gonzalez Smith , J. A. Storer, Parallel algorithms for data compression, Journal of the ACM (JACM), v.32 n.2, p.344-373, April 1985 [doi>10.1145/3149.3152]
• 15: Snort.org. Snort Home Page. http://www.snort.org, 2008.
• 16: C. Spackman. Compression/Decompression Tradeoffs for Data Networking and Storage. http://www.networksystemsdesignline.com/howto/, 2007.
• 17: L. M. Stauffer and D. S. Hirschberg. Parallel Text Compression. Technical report, 1993.
• 18: James A. Storer , John H. Reif, A parallel architecture for high-speed data compression, Journal of Parallel and Distributed Computing, v.13 n.2, p.222-227, Oct. 1991 [doi>10.1016/0743-7315(91)90091-M]
• 19: J. M. Tendler, J. S. Dodson, J. J. S. Fields, and B. Sinharoy. POWER4 System Microarchitecture. IBM Journal of Research and Development, 46(1), 2002.
• 20: R. B. Tremaine, P. A. Franaszek, J. T. Robinson, C. O. Schulz, T. B. Smith, M. E. Wazlowski, and P. M. Bland. IBM Memory Expansion Technology (MXT). IBM Journal of Research and Development, 45(2):271--286, 2001.
• 21: J. Ziv and A. Lempel. A Universal Algorithm for Sequential Data Compression. IEEE Trans. on Information Theory, 23(3):337--343, 1977.

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.3 Network Operations

Additional Classification:
  C. Computer Systems Organization
  C.4 PERFORMANCE OF SYSTEMS

  E. Data
  E.4 CODING AND INFORMATION THEORY
      Subjects: Data compaction and compression

General Terms:
Measurement, Performance