One of commonly used approach to enhance the Web performance is Web proxy caching technique. In Web proxy caching, Least-Frequently-Used-Dynamic-Aging (LFU-DA) is one of the common proxy cache replacement methods, which is widely used in Web proxy cache management. LFU-DA accomplishes a superior byte hit ratio compared to other Web proxy cache replacement algorithms. However, LFU-DA may suffer in hit ratio measure. Therefore, in this paper, LFU-DA is enhanced using popular supervised machine learning techniques such as a support vector machine (SVM), a naive Bayes classifier (NB) and a decision tree (C4.5). SVM, NB and C4.5 are trained from Web proxy logs files and then intelligently incorporated with LFU-DA to form Intelligent Dynamic- Aging (DA) approaches. The simulation results revealed that the proposed intelligent Dynamic- Aging approaches considerably improved the performances in terms of hit and byte hit ratio of the conventional LFU-DA on a range of real datasets.
Koskela, T., Heikkonen, J. and Kaski, K. (2003) Web Cache Optimization with Nonlinear Model Using Object Features. Computer Networks, 43, 805-817. http://dx.doi.org/10.1016/S1389-1286(03)00334-7
Romano, S. and ElAarag, H. (2011) A Neural Network Proxy Cache Replacement Strategy and Its Implementation in the Squid Proxy Server. Neural Computing & Applications, 20, 59-78. http://dx.doi.org/10.1007/s00521-010-0442-0
Cobb, J. and ElAarag, H. (2008) Web Proxy Cache Replacement Scheme Based on Back-Propagation Neural Network. Journal of Systems and Software, 81, 1539-1558. http://dx.doi.org/10.1016/j.jss.2007.10.024
Foong, A.P., Yu-Hen, H. and Heisey, D.M. (1999) Logistic Regression in an Adaptive Web Cache. Internet Computing, 3, 27-36. http://dx.doi.org/10.1109/4236.793455
Sajeev, G. and Sebastian, M. (2011) A Novel Content Classification Scheme for Web Caches. Evolving Systems, 2, 101-118. http://dx.doi.org/10.1007/s12530-010-9026-6
Ali Ahmed, W. and Shamsuddin, S.M. (2011) Neuro-Fuzzy System in Partitioned Client-Side Web Cache. Expert Systems with Applications, 38, 14715-14725. http://dx.doi.org/10.1016/j.eswa.2011.05.009
Ali, W., Sulaiman, S. and Ahmad, N. (2014) Performance Improvement of Least-Recently-Used Policy in Web Proxy Cache Replacement Using Supervised Machine Learning. International Journal of Advances in Soft Computing & Its Applications, 6, 1-38.
Yasin, W., Ibrahim, H., Udzir, N.I. and Hamid, N.A.W.A. (2014) Intelligent Cooperative Least Recently Used Web Caching Policy based on J48 Classifier. 16th International Conference on Information Integration and Web-Based Applications & Services, 262-269. http://dx.doi.org/10.1145/2684200.2684299
Ali, W., Shamsuddin, S.M. and Ismail, A.S. (2011) A Survey of Web Caching and Prefetching. International Journal of Advances in Soft Computing & Its Applications, 3, 18-44.
Wu, X., Kumar, V. and Ross Quinlan, J. (2008) Top 10 Algorithms in Data Mining. Knowledge and Information Systems, 14, 1-37. http://dx.doi.org/10.1007/s10115-007-0114-2
ElAarag, H. (2013) A Quantitative Study of Web Cache Replacement Strategies Using Simulation. In: ElAarag, H., Ed., Web Proxy Cache Replacement Strategies, Springer, London, 17-60. http://dx.doi.org/10.1007/978-1-4471-4893-7_4
Arlitt, M., Friedrich, R. and Jin, T. (2000) Performance Evaluation of Web Proxy Cache Replacement Policies. Performance Evaluation, 39, 149-164. http://dx.doi.org/10.1016/S0166-5316(99)00062-0
Cherkasova, L. (1998) Improving WWW Proxies Performance with Greedy-Dual-Size-Frequency Caching Policy. HP Technical Report, Palo Alto.
Arlitt, M., Cherkasova, L., Dilley, J., Friedrich, R. and Jin, T. (2000) Evaluating Content Management Techniques for Web Proxy Caches. ACM SIGMETRICS Performance Evaluation Review, 27, 3-11. http://dx.doi.org/10.1145/346000.346003
Cao, P. and Irani, S. (1997) Cost-Aware WWW Proxy Caching Algorithms. Proceedings of the 1997 Usenix Symposium on Internet Technology and Systems, Monterey, 8-11 December 1997.
Chen, R.-C. and Hsieh, C.-H. (2006)Web Page Classification Based on a Support Vector Machine Using a Weighted Vote Schema. Expert Systems with Applications, 31, 427-435. http://dx.doi.org/10.1016/j.eswa.2005.09.079
Lu, S.H., Chiang, D.A., Keh, H.C. and Huang, H.H. (2010) Chinese Text Classification by the Naīve Bayes Classifier and the Associative Classifier with Multiple Confidence Threshold Values. Knowledge-Based Systems, 23, 598-604. http://dx.doi.org/10.1016/j.knosys.2010.04.004
Hall, M. (2007) A Decision Tree-Based Attribute Weighting Filter for Naive Bayes. Knowledge-Based Systems, 20, 120-126. http://dx.doi.org/10.1016/j.knosys.2006.11.008
Friedman, N., Geiger, D. and Goldszmidt, M. (1997) Bayesian Network Classifiers. Machine Learning, 29, 131-163. http://dx.doi.org/10.1023/A:1007465528199
Pernkopf, F. (2005) Bayesian Network Classifiers versus Selective K-NN Classifier. Pattern Recognition, 38, 1-10. http://dx.doi.org/10.1016/j.patcog.2004.05.012
Quinlan, J.R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, Burlington.
Huang, C.-J., Wang, Y.-W., Huang, T.-H., Lin, C.-F., Li, C.-Y. and Chen, H.-M. (2011) Applications of Machine Learning Techniques to a Sensor-Network-Based Prosthesis Training System. Applied Soft Computing, 11, 3229-3237. http://dx.doi.org/10.1016/j.asoc.2010.12.025
NLANR (2010) National Lab of Applied Network Research (NLANR). Sanitized Access Logs. http://www.ircache.net/
Chang, C.C. and Lin, C.J. (2001) LIBSVM: A Library for Support Vector Machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm
Hsu, C.W., Chang, C.C. and Lin, C.J. (2009) A Practical Guide to Support Vector Classification. http://www.csie.ntu.edu.tw/~cjlin/libsvm
Fayyad, U.M. and Irani, K.B. (1993) Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI-93), Chambèry, 28 August-3 September 1993, 1022-1027.
Markatchev, N. and Williamson, C. (2002) 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, IEEE Computer Society, 356-363. http://dx.doi.org/10.1109/MASCOT.2002.1167096