The continued increase in demand for objects on the Internet causes high web traffic and consequently low user response time which is one of the major bottleneck in the network world. Increase in bandwidth is a possible solution to the problem but it involves increasing economic cost. An alternative solution is web prefetching. Web prefetching is the process of predicting and fetching web pages in advance by proxy server before a request is sent by a user. Prefetching is performed during the server idle time. Most literature based on the classical prefetch algorithm assumes that the server idle time is large enough to prefetch all user’s predicted requests which is not true in a real life situation. This research aims at improving the web prefetching technique by developing a prefetching technique that can be effective in a high traffic environment when the server idle time is very low.Log files were collected and preprocessed for several client group within a domain. The preprocessed log files were used to create web navigation graph, which shows the transition from one web page to another web page.Support and confidence threshold were used to remove web pages with values less than the threshold values. Several clusters were formed in a particular client group. When the prefetch time is predicted to be too small to prefetch, the entire clusters formed from various domains will be used to create a prioritized cluster based on several user request. The model was evaluated based on hit rate, byte rate, precision, accuracy of prediction and usefulness of prediction. The result shows that the proposed Web Clustering algorithm performs better than the classical prefetch technique when the server idle time is small and behaves same as the classical algorithm as the server time becomes large enough to prefetch all users predictions.

Background of Study

The web is a collection of text documents and other resources, linked by hyperlinks and Uniform Resource Locator (URLs), usually accessed by web browsers, from web servers. The web started from a simple information sharing system, and has now grown to a rich collection of dynamic and interactive services. The tremendous growth of web has resulted into high demand for high bandwidth and delay in fetching user request (Neha, 2013). Users sometimes experience unpredictable delay while retrieving web pages from the server. Increase in bandwidth is a possible solution to the problem but it involves high economic cost. Web caching reduces the latency perceived by the user, reduces bandwidth utilization and reduces the loads on the origin servers (Pallis, 2007). Latency refers to the time elapsed from the time a request is sent to the time sender receives the requested information.

Many latency tolerant techniques have been developed over the years to solve this problem without necessarily increasing the bandwidth. Most notably are caching and prefetching. Web prefetching helps to fetch and cache users request during server idle time, which will reduce the load on the origin server. To reduce the access delay experienced by users, it is advisable to predict and prefetch web object based on user access patterns and cache them. Studies on web pre-fetching are mostly based on the history of user access patterns. If the history information shows an access pattern of URL address A followed B with a high probability, then B will be prefetched once A is accessed (Cheng-Zhong, 2000). Web prefetching is the process of obtaining web pages in advance by proxy server before a request is sent by a user. When a client makes a request for web object, rather than sending request to the web server, it may be fetched from the cache. The main factor for selecting a web pre-fetching algorithm is its ability to predict the web object to be prefetched in order to reduce latency. Web prefetching exploits the spatial locality of web pages, i.e. pages that are linked with current page will be accessed with higher probability than other pages. Web prefetching can be applied in a web environment as between clients and web server, between proxy servers and web server and between clients and proxy server (Greeshma, 2012).

Web prefetching techniques are categorized into probability based and clustering based using weight-functions. In the probability based pre-fetching, probabilities are calculated using the history of data access. This method assumes that the request sequence follows a pattern and calculates the probabilities of following this pattern. Clustering based pre-fetching methods make decisions using the information of the web pages that have been fetched previously, assumes that pages that are close to the previously fetched pages are more likely to be requested in the near future (Greeshma, 2012). Moreover, web prefetching is a research topic that has gained increasing attention in recent years. The web pre-fetching fetches some web objects before users actually request it. Thus, the cache pre-fetching helps on reducing the user perceived latency. Many studies have shown that the combination of caching and pre-fetching doubles the performance compared to single caching (Waleed, 2012).




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