Performance optimization for web caching based on input modeling

Article type: Research Article

Authors: Feng, Wenying^{; *} | Chen, Hua

Affiliations: Department of Computing and Information Systems, Department of Mathematics, Trent University, Peterborough, ON, Canada K9J 7B8

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: In this paper, the adaptive replacement policy and the pre-fetching technique for Web caching systems are studied with two user request models and their combinations. Performance results from two most commonly used replacement policies: LRU (Least Recently Used) and LFU (Least Frequently Used) are compared. It is shown that LRU performs better for one of the user request models and LFU is on the opposite. The adaptive replacement policy is intelligent in finding the best candidate policy as the user access pattern changes. The learning speed of the system is tested by different switch thresholds.

Keywords: LRU, LFU, web caching, threshold, adaptive learning, pre-fetching

DOI: 10.3233/JCM-2009-0243

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 9, no. s2, pp. S149-S157, 2009