R&D: Lazy-WL, Wear-Aware Load Balanced Data Redistribution Method for Efficient SSD Array Scaling

IEEE Xplore has published, in 2021 IEEE International Conference on Cluster Computing (CLUSTER) proceedings, an article written by Hanchen Guo, Zhehan Lin, Yunfei Gu, Shanghai Jiao Tong University, Shanghai, China, Chentao Wu, Shanghai Jiao Tong University, Shanghai, China, and Sichuan Research Institute, Shanghai Jiao Tong University, Sichuan, China, Li Jiang, Shanghai Jiao Tong University, Shanghai, China, and Shanghai Qi Zhi Institute, Shanghai, China, Jie Li, Guangtao Xue, and Minyi Guo, Shanghai Jiao Tong University, Shanghai, China.

Abstract: “Nowadays, Solid State Drive (SSD) arrays have been widely used in commercial big data centers and high-performance storage services. Meanwhile, in the era of explosive data growth, data centers need to implement the array scaling schemes to meet the increasing storage capacity requirements. The existing state-of-the-art scaling methods, such as Round-Robin (RR) and FastScale, aim at ensuring a uniform data redistribution. However, most of them are designed for Hard Disk Drive (HDD) arrays, ignoring lifetime difference among extended and former-used disks, which leads to several additional penalties in SSD arrays. Furthermore, due to the sudden interdisk lifetime disparity, the extended SSD disks trigger frequently wear-leveling operations for controlling the wearing balance into the predefined threshold. These reactions result in inefficient scaling and I/O performance degradation. To address the above problem, we propose a Lazy W ear-L eveling (Lazy-WL) mechanism to reduce the conventional wear-leveling overhead during the scaling process. Its core idea is to reduce the unnecessary intensive wear-leveling migration significantly, via narrowing the difference of program/erase (P/E) cycles among new-added and former deployed disks smoothly and gradually. To demonstrate the effectiveness of this approach, we conduct several simulation via Disksim and real implementation via a Hadoop cluster. The evaluation results show that, compared to the typical inter and intra disk wear leveling methods, Lazy-WL could lower the triggered wear-leveling operations by up to 92.9% and achieve a maximal 85.2% response time reduction, which suggests that Lazy-WL performs a balanced I/O distribution, and maintains high performance of SSD array with high scaling efficiency.“