Adopting a proper cache document replacement policy is critical to the performance of a caching system. Most traditional cache document replacement policies are focused on the efficiency aspect and these documents are replaced according to their last access times, request frequencies, and sizes. However, in addition to providing efficient document acquisition service, a successful commercial website also has to create incentives for customers, so as to gain sufficient revenues to support the continuing operation of the website. For this reason, a good cache document replacement policy has to consider the contribution-to-sales of every document. On the other hand, among the existing cache document replacement policies, no one policy can surpass all the other policies in every case. Besides, the most suitable cache document replacement policy for a caching system is often chosen from the existing policies, which cannot guarantee the optimality of the chosen policy. These phenomena provide a motive to construct a cache document replacement policy which content can be tailored to the specific requirements of a caching system. For these reasons, in this study a tailored cache document replacement policy to promote the sales of a B2C website is proposed. To evaluate the effectiveness of the proposed methodology, an experimental EC website has been constructed, and the log file of the website server was used as the data source to evaluate the performances of various cache document replacement policies under different cache sizes. Four performance measures (including the hit rate, the byte hit rate, the customer hit rate, and the customer byte hit rate) are compared. In our simulation experiments, the proposed policy outperformed the other traditional policies, especially on the two sales-related performance measures (the customer hit rate and the customer byte hit rate).
|Number of pages||11|
|Journal||International Review on Computers and Software|
|State||Published - 1 Sep 2010|
- Cache document replacement policy
- Contribution to sales
- Web mining
- Website efficiency