Reinforcement and Deep Learning Approaches to Dynamic Cache Optimization

Abstract

With explosive data-driven application growth and increasing complexity of contemporary computing environments, conventional static cache management methods fall short more and more. This chapter discusses how reinforcement learning (RL) and deep learning (DL) models are disrupting cache optimization by making caching strategies adaptive, predictive, and context-sensitive. It first discusses basic cache structures and their shortcomings in mobile, cloud, and multicore systems. The discussion then goes on to how RL models such as Q-learning and deep Q-networks learn dynamic optimal cache replacement policies through interaction with the environment, and DL approaches such as long short-term memory, convolutional neural networks, and transformers augment prefetching and eviction prediction based on sequential and pattern recognition abilities. The chapter also points toward online versus offline learning models, feature engineering within cache-aware datasets, and simulation frameworks to measure model accuracy. Real-world case studies and benchmarks demonstrate the hard improvements in hit ratio, latency reduction, and energy efficiency. With the convergence of the power of RL and DL, the chapter highlights a new paradigm of smart cache management that can address the needs of next-generation computing workloads.