Analysis and visualization of proxy caching using LRU, AVL tree and BST with supervised machine learning. (2022)

• Record Type: Journal Article
• Authors: Kushwah, Jitendra Singh; Gupta, Deepak; Shrivastava, Anurag; Ambily Pramitha, P.; Abraham, John T.; Lunagaria, Munindra
• Is Part Of: Materials today. Volume 51:Part 1(2022)
• Page Start: 750
• Page End: 755
• Publication Date: 2022
• Subjects: Proxy Server -- LRU -- AVL tree -- BST -- Access time -- Analysis -- Visualization -- Machine learning; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.06.224
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Proxy cache access speeds and decreases load time. There is an uncertainty regarding cache level 2 which you neglect. This research shall investigate the L1 cache, primary cache, and L2 cache as a secondary proxy server cache. LRU is typically utilised instead of cache. LRU for cold cache removal used to be a time-consuming process, but it isn't particularly efficient today. The performance of the cache L1 used LRU and L2 used LRU_AVL has risen with these solutions. The output is the LRU_AVL among other ways that utilize LRU tables and graphs. proxy cache LRU, LRU_AVL, and LRU_BST have average time of access calculated. Median access time is estimated using Python tools including Pandas, MatPlotLib as well as LRU, LRU_AVL, and LRU_BST. This research will anticipate the average period of usage of LRU, LRU_AVL, and LRU_BST cache algorithms.