Performance Improvement of Proxy Server Cache Management Using Web Usage Mining

In these modern industries, all sectors are transitioning from manual to web-oriented applications. Thus, the number of Internet users are increasing drastically. Therefore, there is a substantial traffic, which increases the demand on the server and server response latency to obtain web objects. The proxy server caching mechanism is one of the approaches to enhance the performance of accessing web objects via the Internet. Since the cache is typically limited in size, a replacement strategy is required to decide which cached web object should be eliminated to allow spaces for fresh web items. Proxy servers make use of various cache replacement strategies such as Least Recency Used (LRU), Least Frequently Used (LFU), and SIZE. The web objects in the proxy cache are influenced by variables like recency, frequency, fetching time, and size. The traditional caching policies decide only one factor at a time, unpopular web objects are wasted in the cache memory (cache pollution) and the performance of the proxy cache decreases. To increase its performance, we propose using the proxy server log file to identify individual users and their sessions as well as categorize the web objects into three groups: high priority, average priority, and low priority web objects. The prepared log file is used to train the classifiers. Future requests are classified as high, average, or low objects using the classifiers, and it is then chosen whether to store them in the proxy cache or not. The objective of this research is to enhance the proxy caching mechanism by implementing the techniques mentioned above. We compared the performance of the suggested approach with traditional caching policies using a trace-driven simulation method. Two performance metrics, Hit Ratio (HR), and Byte Bit Ratio (BHR), were used for our investigation. Our experimental findings demonstrate that the suggested approach outperforms traditional caching policies.