Work-In-Progress: Learned Buffer Management: A New Frontier

Buffer management is a key issue in database systems and operating systems. Recently, with the advance of machine learning, it has been a new frontier to use machine learning techniques to improve buffer management. This paper makes a preliminary study on learned buffer management, aiming to (1) present a conceptual design for learned buffer management and (2) verify whether a machine learning model can help improve the efficiency of buffer management. We first present a framework for learned buffer management, which includes a feature vector-generating Generator, a labeling Predictor, and a decision-making Replacer. Finally, we conduct a preliminary experiment on two tailor-made workloads and the results suggest the efficiency of the learned buffer manager.