An Improved Web Caching System With Locally Normalized User Intervals

• Published in: IEEE access Vol. 9; pp. 112490 - 112501
• Main Authors: Vaithinathan, Krishnakumar, Pernabas, Julian Benadit, Parthiban, Latha, Shrestha, Bhanu, Joshi, Gyanendra Prasad, Moon, Hyeonjoon
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Browsers; Caching; Clustering; Computer architecture; Computer science; Greedy algorithms; Internet of Things; Intervals; Locally normalized intervals; pre-fetching; Prediction algorithms; Prefetching; proxy; Servers; Software-defined networking; Time-frequency analysis; User groups; web cache; Web pages; Websites
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3103804

Caching is one of the most promising areas in the field of future internet architecture like Information-centric Networking, Software Defined Networking, and IoT. In Web caching, most of the web content is readily available across the network, even if the webserver is not reachable. Several existing traditional caching methods and cache replacement strategies are evaluated based on the metrics like hit ratio and byte hit Ratio. However, these metrics have not been improved over the period because of the traditional caching policies. So, in this paper, we have used an intelligent function like locally normalized intervals of page visit, website duration, users' interest between user groups is proposed. These intervals are combined with multiple distance metrics like Manhattan, squared Euclidean, and 3-,4-,5-norm Minkowski. In order to obtain significant common user navigation patterns, the clustering relation between the users using different intervals and distances is thoroughly analyzed. These patterns are successfully coupled with greedy web cache replacement strategies to improve the efficiency of the proposed web cache system. Particularly for improving the caching metrics more, we used an AI-based intelligent approach like Random Forest classifier to boost the prefetch buffer performance and achieves the maximum hit rate of 0.89, 0.90, and byte hit rate of 0.87, 0.89 for Greedy Dual Size Frequency and Weighted Greedy Dual Size Frequency algorithms, respectively. Our experiments show good hit/byte hit rates than the frequently used algorithms like least recently used and least frequently used.