10.35940/ijeat.F2907.0810621
https://zenodo.org/records/5408833
oai:zenodo.org:5408833
Pratheeksha P
Pratheeksha P
Student, Department of Computer Science, RV College of Engineering, Bangalore, India.
Revathi SA
Revathi SA
Assistant Professor, Department of Computer Science, RV College of Engineering, Bangalore, India.
Machine Learning-Based Cache Replacement Policies: A Survey
Zenodo
2021
Belady's algorithm, Cache Replacement, Machine Learning
Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
Publisher
2021-08-30
eng
2249-8958
Creative Commons Attribution 4.0 International
Despite extensive developments in improving cache hit rates, designing an optimal cache replacement policy that mimics Belady’s algorithm still remains a challenging task. Existing standard static replacement policies does not adapt to the dynamic nature of memory access patterns, and the diversity of computer programs only exacerbates the problem. Several factors affect the design of a replacement policy such as hardware upgrades, memory overheads, memory access patterns, model latency, etc. The amalgamation of a fundamental concept like cache replacement with advanced machine learning algorithms provides surprising results and drives the development towards cost-effective solutions. In this paper, we review some of the machine-learning based cache replacement policies that outperformed the static heuristics.