一句话:提出SMR感知的软硬件协同设计缓存算法SAC,优化SMR盘内RMW操作,显著加速混合存储。
SMR磁盘为满足大数据存储需求而被采用,但其写放大和读写延迟极不均衡导致细粒度随机写性能差。现有缓存算法(如LRU提升命中率、MOST降低SMR写放大比)并未针对SMR混合存储性能的关键因素。本文提出SMR感知协同设计缓存算法SAC,通过软硬件协同方法适配SMR特性,重点优化盘内RMW操作,并在数据热度与SMR写放大、干净与脏缓存空间等冲突因素间取得平衡。在真实负载下,SAC在只写模式相比LRU取得7.5倍加速,在读写混合模式取得2.9倍加速。
To satisfy the huge storage capacity requirements of big data, the emerging high-density disks gradually adopt the Shingled Magnetic Recording (SMR) technique. However, the most serious challenge of SMR disks lies in their weak fine-grained random write performance caused by the write amplification inner SMRs and its extremely unbalanced read and write latencies. Although fast storage devices like Flash-based SSDscan be used to boost SMR disks in SMR-based hybrid storage, the optimization targets of existing cache algorithms (e.g., higher popularity for LRU, lower SMR write amplification ratio for MOST) are NOT the crucial factor for the performance of the SMR-based hybrid storage. In this paper, we propose a new SMR-Aware Co-design cache algorithm called SAC to accelerate the SMR-based hybrid storage. SAC adopts a hardware/software co-design method to fit the characteristics of SMR disks and to optimize the crucial factor, i.e., RMW operations inner SMR disks, effectively. Furthermore, SAC also makes a good balance between some conflicting factors, e.g., the data popularity vs. the SMR write amplification and clean cache space vs. dirty cache space. In our evaluations under real-world traces, SAC achieves a 7.5× performance speedup compared with LRU in the write-only mode, and a 2.9× speedup in the read-write mixed mode.
DOI 原文 ·
@inproceedings{paperbot5407,
title = {SAC},
author = {Diansen Sun and Yunpeng Chai},
booktitle = {ASPLOS},
year = {2020},
doi = {10.1145/3373376.3378474}
}