A Two-Level Intelligent Web Caching Scheme with a Hybrid Extreme Learning Machine and Least Frequently Used

Abstract

The immense increase in data traffic has created several issues for the Internet community, including long delays and low throughput. Most Internet user activity occurs via web access, thus making it a major source of Internet traffic. Due to a lack of effective management schemes, Internet usage is inefficient. Advances in caching mechanisms have led to the introduction of web proxies that have improved real-time communication and cost savings. Although several traditional caching polices have been implemented to increase speed and simplicity, cache replacement accuracy remains a key limitation due to cache storage constraints. Our contribution concerns the algorithmic investigation of intelligent soft computing schemes to enhance a web proxy system to improve precision for reproducibility. This research also proposes a two-level caching scheme; the first level is least frequently used (LFU), and an extreme learning machine (ELM) is used for the second level. A traditional ELM for web caching is further optimized with object similarity factors. The proposed scheme is evaluated and compared to a traditional caching policy and its integration with intelligent caching using a well-known dataset from IRCache. The method is shown to achieve good performance in terms of high hit and byte hit rates.