Adaptive Cache Management with Machine Learning

Caching systems require not only a high hit rate but also adaptability and scalability to effectively manage diverse workloads. This study explores the possible directions of using machine learning for adaptive caching. We explore the implementation of several caching strategies from recent literature, including SIEVE, S3-FIFO, and TinyLFU, highlighting the limitations of traditional methods. We leverage the development of these new caching strategies in combination with machine learning to improve adaptability at small cache sizes.We test LeCaR, a model-driven approach that switches between LRU and LFU, on a real-world trace from Microsoft Azure. We build on it by incorporating multiple combinations of traditional and modern methods. We note that modified_LeCaR, with LRU, LFU, and S3-FIFO, tested on real-world traces, demonstrates improved performance over the original LeCaR, particularly in scenarios with smaller cache sizes. modified_LeCaR’s strong performance on smaller cache sizes makes it well-suited for resource-constrained systems (e.g., embedded systems). Especially because we do not use resource-heavy algorithms (e.g., neural networks).