Improving Response Time and Though put of Search Engine with Web Caching

Large web search engines need to be able to process thousands of queries per second on collections of billions of web pages. As a result, query processing is a major performance bottleneck and cost factor in current search engines, and a number of techniques are employed to increase query throughput, including massively parallel processing, index compression, early termination, and caching. Caching is a useful technique for Web systems that are accessed by a large number of users. It enables a shorter average response time, it reduces the workload on back-end servers, and it reduces the overall amount of utilized bandwidth. Our contribution in this paper can be split into two parts. In the first part, we proposed Cached Search Algorithm (CSA) on top of the multiple search engines like Google, Yahoo and Bing and achieved the better response time while accessing the resulting web pages. In the second part, we design and implemented the Cached Search Engine and the performance evaluated based on the training data (WEPS dataset [1]) and the test data (Mobile dataset). The Cached Search outperforms the better by reducing the response time of search engine and to increase response throughput of the searched results