Work-in-Progress: A Deep Learning Strategy for I/O Scheduling in Storage Systems
Ashkan Farhangi, Jiang Bian, Jun Wang, Zhishan Guo · 2019
Under the big data era, there is a crucial need to improve the performance of storage systems for data-intensive applications. Data-intensive applications tend to behave in a predictable manner, which can be exploited for improving the performance of the storage system. At the storage level, we propose a deep recurrent neural network that learns the patterns of I/O requests and predicts the upcoming ones, such that memory contents can be pre-loaded at the right time to prevent cache/memory misses. Preliminary experimental results, on two real-world I/O logs of storage systems (from financial and web search), are reported-they partially demonstrate the effectiveness of the proposed method.