Improving the Performance of a Proxy Cache Using Expectation Maximization with Naive Bayes Classifier

  • P. Julian Benadit
  • F. Sagayaraj Francis
  • U. Muruganantham
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)

Abstract

The Expectation Maximization Naive Bayes classifier has been a centre of attention in the area of Web data classification. In this work, we seek to improve the operation of the traditional Web proxy cache replacement policies such as LRU and GDSF by assimilating semi supervised machine learning technique for raising the operation of the Web proxy cache. Web proxy caching is utilized to improve performance of the Proxy server. Web proxy cache reduces both network traffic and response period. In the beginning section of this paper, semi supervised learning method as an Expectation Maximization Naive Bayes classifier (EM-NB) to train from proxy log files and predict the class of web objects to be revisited or not. In the second part, an Expectation Multinomial Naïve Bayes classifier (EM-NB) is incorporated with traditional Web proxy caching policies to form novel caching approaches known as EMNB-LRU and EMNB-GDSF. These proposed EMNB-LRU and EMNB-GDSF significantly improve the performances of LRU and GDSF respectively.

Keywords

Web caching Proxy server Cache replacement Classification Expectation Maximization Naive Bayes classifier 

References

  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, pp. 101–103. Morgan Kaufmann, Burlington (2001)Google Scholar
  2. 2.
    Cherkasova, L.: Improving WWW Proxies Performance with Greedy-Dual-Size-Frequency Caching Policy. Technical Report HPL-98-69R1. Hewlett-Packard Laboratories, Nov 1998Google Scholar
  3. 3.
    Ali, W., Shamsuddin, S.M., Ismail, A.S.: Intelligent Naïve Bayes-based approaches for web proxy caching. Knowl. Based Syst. 31, 162–175 (2012)CrossRefGoogle Scholar
  4. 4.
    Romano, S., ElAarag, H.: A neural network proxy cache replacement strategy and its implementation in the squid proxy server. Neural Comput. Appl. 20, 59–78 (2011)CrossRefGoogle Scholar
  5. 5.
    Kumar, C., Norris, J.B.: A new approach for a proxy-level web caching mechanism. Decis. Support Syst. 46, 52–60 (2008)CrossRefGoogle Scholar
  6. 6.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Burlington (1993)Google Scholar
  7. 7.
    Ali Ahmed, W., Shamsuddin, S.M.: Neuro-fuzzy system in partitioned client side web cache. Expert Syst. Appl. 38, 14715–14725 (2011)CrossRefGoogle Scholar
  8. 8.
    Chen, H.T.: Pre-fetching and re-fetching in web caching system. Algorithms and Simulation, Master thesis, Trent University, Peterborough, Ontario (2008)Google Scholar
  9. 9.
    Liu, B.: Web Data Mining: Exploiting Hyperlinks, Contents, and Usage Data, pp. 173–176. Springer, Berlin (2007)Google Scholar
  10. 10.
    Podlipnig, S., Boszormenyi, L.: A survey of web cache replacement strategies. ACM Comput. Surv. 35, 374–398 (2003)CrossRefGoogle Scholar
  11. 11.
    NLANR.: National Lab of Applied Network Research (NLANR), and Sanitized Access Logs. Available at http://www.ircache.net/2010
  12. 12.
    Markatchev, N., Williamson, C.: WebTraff: a GUI for web proxy cache workload modeling and analysis. In: Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, pp. 356–363. IEEE Computer Society (2002)Google Scholar
  13. 13.
    Kin-Yeung, W.: Web cache replacement policies a pragmatic approach. IEEE Netw. 20, 28–34 (2006)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • P. Julian Benadit
    • 1
  • F. Sagayaraj Francis
    • 1
  • U. Muruganantham
    • 2
  1. 1.Department of Computer Science and EngineeringPondicherry Engineering CollegePondicherryIndia
  2. 2.Department of Computer Science and EngineeringPondicherry UniversityPondicherryIndia

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