Learning Future Reference Patterns for Efficient Cache Replacement Decisions

This study proposes a cache replacement policy technique to increase the cache hit rate. This policy can improve the efficiency of cache management and performance. Heuristic cache replacement policies are mechanisms that are designed empirically in advance to determine what needs to be replaced. This study explains why the heuristic policy does not achieve a high accuracy for certain patterns of data. A machine learning method is proposed to predict the blocks that need to be requested in the future to prevent erroneous decisions. The core operation of the proposed method is that when a cache miss occurs, the machine learning model predicts a future block reference sequence that is based on the block reference sequence of the input sequence. The predicted block is added to the prediction buffer and the predicted block is removed from the non-access buffer if it exists in the non-access buffer. After filling the prediction buffer, the conventional replacement policy can be replaced with a time complexity of O(1) by replacing the block with a non-access buffer. The proposed method improves the least recently used (LRU) algorithm by 77%, the least frequently used (LFU) algorithm by 65%, and the adaptive replacement cache (ARC) by 77% and shows a hit rate similar to that of state-of-the-art research. The proposed method reinforces the existing heuristic policy and enables a consistent performance for LRU- and LFU-friendly workloads.