Intelligent Web caching using neurocomputing and particle swarm optimization algorithm
Web caching is a technology for improving network traffic on the internet. It is a temporary storage of Web objects (such as HTML documents) for later retrieval. There are three significant advantages to Web caching; reduced bandwidth consumption, reduced server load, and reduced latency. These rewa...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2008
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/7770/1/Sulaiman_Sarina_2008_Intelligent_Web_Caching_Using_Neurocomputing.pdf http://eprints.utm.my/id/eprint/7770/ http://dx.doi.org/10.1109/AMS.2008.40 |
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| Institution: | Universiti Teknologi Malaysia |
| Language: | English |
| Summary: | Web caching is a technology for improving network traffic on the internet. It is a temporary storage of Web objects (such as HTML documents) for later retrieval. There are three significant advantages to Web caching; reduced bandwidth consumption, reduced server load, and reduced latency. These rewards have made the Web less expensive with better performance. In this paper, an Artificial Intelligence (AI) approach is introduced for Web caching to determine the type of Web request, either to cache or not, and to optimize the performance on Web cache. Two methods are employed in this study; Artificial Neural Network (ANN), and Particle Swarm Optimization (PSO). The experimental results have revealed that some improvements have been accomplished compared to the existing technique in terms of Web cache size. |
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