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Clustering Based Learning Approach for Ant Colony Optimization Modelto Simulate Web User Behavior



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Recent times the massive use of web has increased the traffic in network and also the load the web servers manages. Even though nowadays web users have made higher bandwidth connections, they still perceive high latencies when navigating the web due to overloaded elements (e.g., network, servers, switches, or intermediate hardware), long message transference times, and the Round Trip Time (RTT). As a result, the reduction of the users perceived latency when browsing the web is still a crucial research issue. The reduction of the web users’ perceived latency has become the subject of many research efforts over the last few years. The widely used techniques proposed to reduce this latency are web caching, geographical replication, and pre-fetching. Caching techniques are widely implemented now days since they achieve important latency savings. Many Multinational companies implement web replication by using CDNs (Content Delivery Networks) [02] to reduce their websites access time, but this solution is not feasible as it is expensive and many small companies, organizations cannot afford it. Web pre-fetching techniques are mutually independent to caching and replication techniques, so that they can be applied together to achieve a better web performance. Caching and replication techniques have been widely implemented in real world, some studies have also investigated web pre-fetching in real environments.

World Wide Web is a repository of data which is accessible by Web Browser. The WWW is a part of Internet. The Internet is physical as it consists of cable, router, switch etc but WWW is a just a concept. The Internet is the actual network of networks where all the information presents. There are two protocols which are used to transfer Web pages over the Network namely Hyper-Text Transfer Protocol (HTTP) and File Transfer Protocol (FTP). Hypertext is a text which makes use of an address of another file or data.

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Software Requirement :   MATLAB

Hardware Requirement :   • Intel Processor 2.0 GHz or above. • 2 GB RAM or more. • 160 GB or more Hard Disk Drive or above.

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