Application of Machine Learning Algorithms in Data Cache and Storage Hierarchical Management

Existing data cache and storage hierarchy management methods often face problems such as insufficient cache replacement strategy, inflexible cache size adjustment, and difficulty in optimizing storage hierarchy selection. These problems lead to slow system response, low cache hit rate, and waste of storage resources. To solve these problems, this paper proposes a dynamic cache and storage hierarchy management method based on machine learning algorithm. Specifically, this paper uses reinforcement learning (Q-learning and deep Q network DQN) to optimize cache replacement strategy, uses K-means clustering and neural network algorithm to dynamically adjust cache size, and uses Long Short-Term Memory (LSTM) for cache prediction and prefetching. In addition, this paper also combines decision trees and Support Vector Machine (SVM) to optimize storage tier selection, and performs automatic data migration and mobility optimization through cluster analysis. Through experimental verification, machine learning algorithms can significantly improve cache hit rates, reduce access latency, and optimize storage resource utilization in cache management. These results show that cache and storage tier management strategies based on machine learning have strong performance improvement potential and can effectively improve data access efficiency and system performance.