
TL;DR
This paper introduces a novel cache replacement algorithm using Probabilistic Graphical Models, specifically Bayesian networks, to predict future requests, resulting in a 7% increase in cache hits over classical methods.
Contribution
The research applies Bayesian network techniques to cache replacement, improving cache hit rates without increasing cache size or overhead.
Findings
Achieved 7% more cache hits than classical algorithms
Pre-eviction combined with PGM enhances cache performance
Method maintains low overhead while improving efficiency
Abstract
Cache replacement algorithms are used to optimize the time taken by processor to process the information by storing the information needed by processor at that time and possibly in future so that if processor needs that information, it can be provided immediately. There are a number of techniques (LIFO, FIFO, LRU, MRU, Hybrid) used to organize information in such a way that processor remains busy almost all the time. But there are some limitations of every technique. We tried to overcome those limitations. We used Probabilistic Graphical Model(PGM), which gives conditional dependency between random variables using directed or undirected graph. In our research, we exploited the Bayesian network technique to predict the future request by processor. The main goal of the research was to increase the cache hit rate but not by increasing the size of cache and also reducing or maintaining the…
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Taxonomy
TopicsCaching and Content Delivery · Algorithms and Data Compression · Advanced Data Storage Technologies
