BPP: A Realtime Block Access Pattern Mining Scheme for I/O Prediction

Block access patterns refer to the regularities of accessed blocks, and can be used to effectively enhance the intelligence of block storage systems. However, existing algorithms fail to uncover block access patterns in efficient ways. They either suffer high time and space overhead or only focus on the simplest patterns like sequential ones. In this paper, we propose a realtime block access pattern mining scheme, called BPP, to mine block access patterns at run time with low time and space overhead for making efficient I/O predictions. To reduce the time and space overhead for mining block access patterns, BPP classifies block access patterns into simple and compound ones based on the mining costs of different patterns, and differentiates the mining policies for simple and compound patterns. BPP also adopts a novel garbage cleaning policy, which is specially designed based on the observed features of the obtained patterns to accurately detect valueless patterns and remove them as early as possible. With such a garbage cleaning policy, BPP further reduces the space overhead for managing and utilizing the obtained patterns. To demonstrate the effect of BPP, we conduct a series of experiments with real-world workloads. The experimental results show that BPP can significantly outperform the state-of-the-art I/O prediction schemes.