Recently, the machine learning research trend expands to the system performance optimization field, where it has still been proposed by researchers based on their intuitions and heuristics. Compared to conventional major machine learning research areas such as image or speech recognition, machine learning-based system performance optimization fields are at the beginning stage. However, recent papers show that this approach is promising and has significant potential. This paper reviews 11 machine learning-based system performance optimization approaches from nine recent papers based on well-known machine learning models such as perceptron, LSTM, and RNN. This survey provides a detailed design and summarizes model, input, output, and prediction method of each approach. This paper covers various system performance areas from the data structure to essential system components of a computer system such as index structure, branch predictor, sort, and cache management. The result shows that machine learning-based system performance optimization has an important potential for future research. We expect that this paper shows a wide range of applicability of machine learning technology and provides a new perspective for system performance optimization.

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.

Corrigendum: Integrating a Statistical Topic Model and a Diagnostic Classification Model for Analyzing Items in a Mixed Format Assessment