Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9951437 | Future Generation Computer Systems | 2019 | 10 Pages |
Abstract
In this paper, we propose a sequentiality-aware identification scheme for performance-critical data, called FvRS, which boots the accuracy of I/O cost evaluation by exploiting data's access sequentiality. The key idea is to evaluate data's I/O cost based on both request size and access sequentiality. By properly identifying high-cost hot data, FvRS maximizes the utilization of SSD to improve system performance. In addition, FvRS maintains performance-critical data in a real-time table to reduce the identification overhead. We have implemented FvRS in a hybrid storage system in Linux. Extensive evaluations using three real-workload traces and a famous benchmark Postmark demonstrate the accuracy and efficiency of FvRS. Compared with the state-of-the-art schemes, such as hotness-based identification and cost-based identification, FvRS reduces I/O response time by 10.3%â¼45.6% and 16.3%â¼25.1%, respectively.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Gaoxiang Xu, Zhipeng Tan, Dan Feng, Laurence T. Yang, Wei Zhou, Xinyan Zhang, Yang Zhang, Jie Xu,