Towards a Scalable, Distributed Metadata Service for Causal Consistency under Partial Geo-replication
Manuel Bravo, Luı́s Rodrigues, Peter Van Roy · 2015
Causal consistency is a consistency criteria of practical relevance in geo-replicated settings because it provides well-defined semantics in a scalable manner. In fact, it has been proved that causal consistency is the strongest consistency model that can be enforced in an always-available system. Previous approaches to provide causal consistency, which successfully tackle the problem under full geo-replication, have unveiled the inherent tradeoff between the concurrency that the system allows and the size of the metadata needed to enforce causality. When the metadata is compressed, information about concurrency may be lost, creating false dependencies, i.e., the encoding may suggest a causal relation that does not exist in reality. False dependencies may cause artificial delays when processing requests, and decrease the quality of service experienced by the clients.