ReHSS: Optimizing Latency for Cloud Hybrid Storage Systems Using in-Network Placement

Wenjie Wang, Bo Peng, Jianguo Yao, Haibing Guan

Abstract: Modern cloud hybrid storage systems have been concentrating on strategic data placement to provide low disk I/O latency for various workloads. However, previous studies primarily focus on exploring adaptive data placement algorithms with high placement accuracy, overlooking the computing latency introduced by these algorithms. It significantly increases the end-to-end latency that determines the quality of service (QoS) for hybrid storage systems. We propose ReHSS, a novel in-network data placement framework that optimizes the end-to-end latency for hybrid storage systems using modern SmartNIC. We first investigate the overhead of adaptive data placement for hybrid storage systems. Then, we propose a comprehensive hardware/software co-optimization solution based on in-network processing that includes algorithm acceleration, data transmission and processing, and computing and communication overlapping. Experimental results present that compared to the SOTA solution, ReHSS optimizes the end-to-end latency for hybrid storage systems by $1.54 \times \sim 15.18 \times$.

External IDs:dblp:conf/iwqos/WangPYG25