ICIEIS 2011: Informatics Engineering and Information Science pp 559-572 | Cite as
Intelligent Web Proxy Caching Approaches Based on Support Vector Machine
Abstract
Web proxy caching is one of the most successful solutions for improving the performance of Web-based systems. In Web proxy caching, the popular web objects that are likely to be revisited in the near future are stored on the proxy server which plays the key roles between users and web sites in reducing the response time of user requests and saving the network bandwidth. However, the difficulty in determining the ideal web objects that will be re-visited in the future is still a problem faced by existing conventional Web proxy caching techniques. In this paper, support vector machine (SVM) is used to enhance the performance of conventional web proxy caching such as Least-Recently-Used (LRU) and Greedy-Dual-Size-Frequency (GDSF). SVM is intelligently incorporated with conventional Web proxy caching techniques to form intelligent caching approaches called SVM_LRU and SVM_GDSF with better performance. Experimental results have revealed that the proposed SVM_LRU and SVM_GDSF improve significantly the performances of LRU and GDSF respectively across several proxy datasets.
Keywords
Web proxy caching Cache replacement Support vector machinePreview
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References
- 1.Koskela, T., Heikkonen, J., Kaski, K.: Web cache optimization with nonlinear model using object features. Computer Networks 43(6), 805–817 (2003)CrossRefMATHGoogle Scholar
- 2.Chen, T.: Obtaining the optimal cache document replacement policy for the caching system of an EC website. European Journal of Operational Research 181(2), 828–841 (2007)CrossRefMATHGoogle Scholar
- 3.Romano, S., ElAarag, H.: A neural network proxy cache replacement strategy and its implementation in the Squid proxy server. Neural Computing & Applications 20(1), 59–78 (2011)CrossRefGoogle Scholar
- 4.Kaya, C.C., Zhang, G., Tan, Y., Mookerjee, V.S.: An admission-control technique for delay reduction in proxy caching. Decision Support Systems 46(2), 594–603 (2009)CrossRefGoogle Scholar
- 5.Cobb, J., ElAarag, H.: Web proxy cache replacement scheme based on back-propagation neural network. Journal of Systems and Software 81(9), 1539–1558 (2008)CrossRefGoogle Scholar
- 6.Kin-Yeung, W.: Web cache replacement policies: a pragmatic approach. IEEE Network 20(1), 28–34 (2006)CrossRefGoogle Scholar
- 7.Ali, W., Shamsuddin, S.M., Ismail, A.S.: Web proxy cache content classification based on support vector machine. Journal of Artificial Intelligence 4(1), 100–109 (2011)CrossRefGoogle Scholar
- 8.Kumar, C., Norris, J.B.: A new approach for a proxy-level web caching mechanism. Decision Support Systems 46(1), 52–60 (2008)CrossRefGoogle Scholar
- 9.Ali, W., Shamsuddin, S.M.: Intelligent client-side Web caching scheme based on least recently used algorithm and neuro-fuzzy system. In: Yu, W., He, H., Zhang, N. (eds.) ISNN 2009. LNCS, vol. 5552, pp. 70–79. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 10.Sulaiman, S., Shamsuddin, S.M., Forkan, F., Abraham, A.: Intelligent Web caching using neurocomputing and particle swarm optimization algorithm. In: Second Asia International Conference on Modeling & Simulation, AICMS 2008 (2008)Google Scholar
- 11.Chen, R.-C., Hsieh, C.-H.: Web page classification based on a support vector machine using a weighted vote schema. Expert Systems with Applications 31(2), 427–435 (2006)CrossRefGoogle Scholar
- 12.Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, Heidelberg (2007)MATHGoogle Scholar
- 13.Kumar, C.: Performance evaluation for implementations of a network of proxy caches. Decision Support Systems 46(2), 492–500 (2009)CrossRefGoogle Scholar
- 14.Cao, P., Irani, S.: Cost-aware WWW proxy caching algorithms. In: Proceedings of the 1997 Usenix Symposium on Internet Technology and Systems, Monterey, CA (1997)Google Scholar
- 15.Cherkasova, L.: Improving WWW proxies performance with Greedy-Dual-Size-Frequency caching policy. In: HP Technical Report, Palo Alto (1998)Google Scholar
- 16.Vakali, A.: Evolutionary techniques for Web caching. Distrib. Parallel Databases 11(1), 93–116 (2002)CrossRefMATHGoogle Scholar
- 17.Podlipnig, S., Böszörmenyi, L.: A survey of Web cache replacement strategies. ACM Comput. Surv. 35(4), 374–398 (2003)CrossRefGoogle Scholar
- 18.Ali, W., Shamsuddin, S.M., Ismail, A.S.: A survey of Web caching and prefetching. Int. J. Advance. Soft Comput. Appl. 3(1), 18–44 (2011)Google Scholar
- 19.Vapnik, V.: The nature of statistical learning theory. Springer, New York (1995)CrossRefMATHGoogle Scholar
- 20.NLANR, National Lab of Applied Network Research (NLANR). Sanitized access logs (2010), http://www.ircache.net/
- 21.ElAarag, H., Romano, S.: Improvement of the neural network proxy cache replacement strategy. In: Proceedings of the 2009 Spring Simulation Multiconference, pp. 1–8. Society for Computer Simulation International, San Diego (2009)Google Scholar
- 22.Foong, A.P., Yu-Hen, H., Heisey, D.M.: Logistic regression in an adaptive Web cache. IEEE Internet Computing 3(5), 27–36 (1999)CrossRefGoogle Scholar
- 23.Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification (2009)Google Scholar
- 24.Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
- 25.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