Self-tuning eventually-consistent data stores

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 focus on the consistency-latency trade-off, which dictates that a distributed storage system can either guarantee that clients always see the latest data, or it can guarantee that operation latencies are small (relative to the inter-data-center latencies) but not both. We propose a technique called spectral shifting for tuning this trade-off adaptively to meet an application-specific performance target in a dynamically changing environment. Experiments conducted in a real wold cloud computing environment demonstrate that our tuning framework provides superior convergence compared to a state-of-the-art solution.