WALOR: Workload-Driven Adaptive Layout Optimization of Raft Groups for Heterogeneous Distributed Key-Value Stores

Yangyang Wang, Yunpeng Chai, Qingpeng Zhang

Abstract: In a heterogeneous cluster based on the Raft protocol, in order to solve the problem of slow performance caused by the leader on a slow node, someone proposed ALOR. However, the leader distribution of ALOR is not optimal. In this paper, we propose Workload-driven Adaptive Layout Optimization of Raft groups (WALOR), which changes the leader distribution of ALOR to promote the performance further by more fitting the read-write request ratio of the system’s workload. Our experiments on an actual heterogeneous cluster show that, on average, WALOR improves throughput by 82.96% and 32.42% compared to the even distribution (ED) solution and ALOR, respectively.