Intelligent Client-Side Web Caching Scheme Based on Least Recently Used Algorithm and Neuro-Fuzzy System

  • Waleed Ali
  • Siti Mariyam Shamsuddin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)


Web caching is a well-known strategy for improving performance of Web-based system by keeping web objects that are likely to be used in the near future close to the client. Most of the current Web browsers still employ traditional caching policies that are not efficient in web caching. This research proposes a splitting client-side web cache to two caches, short-term cache and long-term cache. Primarily, a web object is stored in short-term cache, and the web objects that are visited more than the pre-specified threshold value will be moved to long-term cache, while other objects are removed by Least Recently Used(LRU) algorithm as short-term cache is full. More significantly, when the long-term cache saturates, the trained neuro-fuzzy system is employed in classifying each object stored in long-term cache into cacheable or uncacheable object. The old uncacheable objects are candidate for removing from the long-term cache. By implementing this mechanism, the cache pollution can be mitigated and the cache space can be utilized effectively. Experimental results have revealed that the proposed approach has better performance compared to the most common caching policies and has improved the performance of client-side caching substantially.


Client-side web caching Adaptive neuro-fuzzy inference system Least Recently Used algorithm 


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  1. 1.
    Wessels, L.D.: Web Caching. O’Reilly, USA (2001)Google Scholar
  2. 2.
    Chen, H.T.: Pre-fetching and Re-fetching in Web Caching systems: Algorithms and Simulation. Master Thesis, TRENT UNIVESITY, Peterborough, Ontario, Canada (2008)Google Scholar
  3. 3.
    Mookerjee, V.S., Tan, Y.: Analysis of a Least Recently used Cache Management Policy for Web Browsers. Operations Research, Linthicum 50(2), 345–357 (2002)CrossRefGoogle Scholar
  4. 4.
    Chen, T.: Obtaining the Optimal Cache Document Replacement Policy for the Caching System of an EC Website. European Journal of Operational Research 181(2), 828 (2007)CrossRefzbMATHGoogle Scholar
  5. 5.
    Koskela, T., Heikkonen, J., Kaski, K.: Web Cache Optimization with Nonlinear Model using Object Feature. Computer Networks Journal 43(6) (2003)Google Scholar
  6. 6.
    Ayani, R., Teo, Y.M., Ng, Y.S.: Cache pollution in Web Proxy Servers. In: International Parallel and Distributed Processing Symposium (IPDPS 2003), p. 248a. ipdps (2003)Google Scholar
  7. 7.
    Wong, A.K.Y.: Web Cache Replacement Policies: A Pragmatic Approach. IEEE Network magazine 20(1), 28–34 (2006)CrossRefGoogle Scholar
  8. 8.
    Podlipnig, S., Böszörmenyi, L.: A Survey of Web Cache Replacement Strategies. ACM Computing Surveys 35(4), 374–398 (2003)CrossRefGoogle Scholar
  9. 9.
    Cobb, J., ElAarag, H.: Web Proxy Cache Replacement Scheme based on Back-propagation Neural Network. Journal of System and Software (2007)Google Scholar
  10. 10.
    Farhan: Intelligent Web Caching Architecture, Master thesis, Faculty of Computer Science and Information System, UTM university, Johor, Malaysia (2007)Google Scholar
  11. 11.
    Acharjee, U.: Personalized and Artificial Intelligence Web Caching and Prefetching. Master thesis. University of Ottawa, Canada (2006)Google Scholar
  12. 12.
    Li, X.X., Huang, H., Liu, C.H.: The Application of an ANFIS and BP Neural Network Method in Vehicle Shift Decision. In: 12th IFToMM World Congress, Besançon France, M.C (2007)Google Scholar
  13. 13.
    Calzarossa, V.G.: A Fuzzy Algorithm for Web Caching. Simulation Series Journal 35(4), 630–636 (2003)Google Scholar
  14. 14.
    Krishnamurthy, B., Rexforrd, J.: Web Protocols and Practice: HTTP/1.1, Networking Protocols, Caching and Traffic Measurement. Addison-Wesley, Reading (2001)Google Scholar
  15. 15.
    Muñoz-Expósito, J.E., García-Galán, S., Ruiz-Reyes, N., Vera-Candeas, P.: Adaptive Network-based Fuzzy Inference System vs. Other Classification Algorithms for Warped LPC-based Speech/music Discrimination. Engineering Applications of Artificial Intelligence 20(6), 783–793 (2007)CrossRefGoogle Scholar
  16. 16.
    Jang: ANFIS: Adaptive-network-based Fuzzy Inference System. IEEE Trans. Syst. Man Cybern. 23(3), 665 (1993)CrossRefGoogle Scholar
  17. 17.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Waleed Ali
    • 1
  • Siti Mariyam Shamsuddin
    • 1
  1. 1.Faculty of Computer Science and Information SystemUTM UniversityJohorMalaysia

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