Efficient Causal Access in Geo-Replicated Storage Systems

Abstract

We consider a setting where applications, such as websites or games, need causal access to objects available in geo-replicated cloud data stores. Common ways of implementing causal consistency involve hiding objects while waiting for their dependencies or waiting for server replicas to synchronize. To minimize delays and retrieve objects faster, applications may try to reach different server replicas at once. This entails a cost because providers charge for each reading request, including reading misses where the causal copy of the object is unavailable. Therefore, latency and cost are conflicting goals, which we control by selecting where to read and when. We formulate this challenge as a multi-criteria optimization problem and propose five non-dominated reading strategies, four of which are Pareto optimal, in a setting constrained to two server replicas. We validate these solutions on the following real cloud storage services: AWS S3, DynamoDB and MongoDB. Savings of as much as 50% on reading costs, with no significant or even a positive impact on latency, demonstrate that both clients and cloud providers could benefit from richer services compatible with these retrieval strategies.