Rough web caching

The demand for Internet content rose dramatically in recent years. Servers became more and more powerful and the bandwidth of end user connections and backbones grew constantly during the last decade. Nevertheless users often experience poor performance when they access web sites or download files....

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Main Authors: Sulaiman, Sarina, Shamsuddin, Siti Mariyam, Abraham, Ajith
Format: Article
Language:English
Published: Springer Berlin / Heidelberg 2009
Subjects:
Online Access:http://eprints.utm.my/7763/
http://eprints.utm.my/7763/1/Sulaiman_Sarina_2009_Rough_Web_Caching.pdf
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author Sulaiman, Sarina
Shamsuddin, Siti Mariyam
Abraham, Ajith
author_facet Sulaiman, Sarina
Shamsuddin, Siti Mariyam
Abraham, Ajith
author_sort Sulaiman, Sarina
building UTeM Institutional Repository
collection Online Access
description The demand for Internet content rose dramatically in recent years. Servers became more and more powerful and the bandwidth of end user connections and backbones grew constantly during the last decade. Nevertheless users often experience poor performance when they access web sites or download files. Reasons for such problems are often performance problems, which occur directly on the servers (e.g. poor performance of server-side applications or during flash crowds) and problems concerning the network infrastructure (e.g. long geographical distances, network overloads, etc.). Web caching and prefetching have been recognized as the effective schemes to alleviate the service bottleneck and to minimize the user access latency and reduce the network traffic. In this chapter, we model the uncertainty in Web caching using the granularity of rough set (RS) and inductive learning. The proposed framework is illustrated using the trace-based experiments from Boston University Web trace data set.
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spelling utm-77632010-06-02T01:47:54Z http://eprints.utm.my/7763/ Rough web caching Sulaiman, Sarina Shamsuddin, Siti Mariyam Abraham, Ajith QA75 Electronic computers. Computer science The demand for Internet content rose dramatically in recent years. Servers became more and more powerful and the bandwidth of end user connections and backbones grew constantly during the last decade. Nevertheless users often experience poor performance when they access web sites or download files. Reasons for such problems are often performance problems, which occur directly on the servers (e.g. poor performance of server-side applications or during flash crowds) and problems concerning the network infrastructure (e.g. long geographical distances, network overloads, etc.). Web caching and prefetching have been recognized as the effective schemes to alleviate the service bottleneck and to minimize the user access latency and reduce the network traffic. In this chapter, we model the uncertainty in Web caching using the granularity of rough set (RS) and inductive learning. The proposed framework is illustrated using the trace-based experiments from Boston University Web trace data set. Springer Berlin / Heidelberg 2009 Article PeerReviewed application/pdf en http://eprints.utm.my/7763/1/Sulaiman_Sarina_2009_Rough_Web_Caching.pdf Sulaiman, Sarina and Shamsuddin, Siti Mariyam and Abraham, Ajith (2009) Rough web caching. Studies in Computational Intelligence, 174 . pp. 187-211. ISSN 1860-949X (Print) 1860-9503 (Online) http://dx.doi.org/10.1007/978-3-540-89921-1_7 10.1007/978-3-540-89921-1_7
spellingShingle QA75 Electronic computers. Computer science
Sulaiman, Sarina
Shamsuddin, Siti Mariyam
Abraham, Ajith
Rough web caching
title Rough web caching
title_full Rough web caching
title_fullStr Rough web caching
title_full_unstemmed Rough web caching
title_short Rough web caching
title_sort rough web caching
topic QA75 Electronic computers. Computer science
url http://eprints.utm.my/7763/
http://eprints.utm.my/7763/
http://eprints.utm.my/7763/
http://eprints.utm.my/7763/1/Sulaiman_Sarina_2009_Rough_Web_Caching.pdf