Brief Announcement: A Probabilistic Performance Model and Tuning Framework for Eventually Consistent Distributed Storage Systems

Abstract

Replication protocols in distributed storage systems are fundamentally constrained by the finite propagation speed of information, which necessitates trade-offs among performance metrics even in the absence of failures. We make two contributions toward a clearer understanding of such trade-offs. First, we introduce a probabilistic model of eventual consistency that captures precisely the relationship between the workload, the network latency, and the consistency observed by clients. Second, we propose a technique for adaptive tuning of the consistency-latency trade-off that is based partly on measurement and partly on mathematical modeling. Experiments demonstrate that our probabilistic model predicts the behavior of a practical storage system accurately for low levels of throughput, and that our tuning framework provides superior convergence compared to a state-of-the-art solution.