Abstract
In this paper Web cache optimization using document features is proposed. The problem in Web cache optimization is to decide which strategy to use in replacement of cache objects. While commonly used policies use heuristic rules, proposed model predicts the value of each Web object by using features collected from the HTTP responses and from the HTML structure of the document. In a case study, generalized linear model and multilayer perceptron committee model are used to classify about 50000 Web documents according to their popularity. Results show that linear model does not find any correlation between the features and document popularity. MLP model gives better results, yielding mean classification percentages of 64 and 74 for the documents to be left or to be removed from the Web cache, respectively.
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References
Douglis, F., Feldmann, A., Krishnamurthy, B., Mogul, J.: Rate of Change and Other Metrics: a Live Study of the World Wide Web. USENIX Symposium on Internet Technologies and Systems (1997)
Williams, S., Abrams, M., Standridge, R.G., Abdulla, G., and Fox, E.A.: Removal Policies in Network Caches for World-Wide Web Documents. ACM SIGCOMM’96 Conference (1996)
Davison, B.D.: A Survey of Proxy Cache Evaluation Techniques. 4th International Web Caching Workshop (1999)
Cao, P., Irani, S.: Cost-Aware WWW Proxy Caching Algorithms. USENIX Symposium on Internet Technologies and Systems (1997)
Rizzo, L., Vicisano, L.. Replacement Policies for a Proxy Cache. IEEE/ACM Transactions on Networking, 8(2) (2000) 158–170
Foong, A.P., Hu, Y-H., Heisey, D.M.: Logistic Regression in an Adaptive Web Cache. IEEE Internet Computing 3(5) (1999) 27–36
Koskela, T., Heikkonen, J., Kaski, K.: Modeling the Cacheability of HTML Documents. 9th International World Wide Web Conference (2000)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press (1995)
Riedmiller, M., Braun, H.: A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. IEEE International Conference on Neural Networks (1993) 586–591
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© 2001 Springer-Verlag Berlin Heidelberg
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Koskela, T., Heikkonen, J., Kaski, K. (2001). Using Document Features to Optimize Web Cache. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_169
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DOI: https://doi.org/10.1007/3-540-44668-0_169
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