Fisheye Consistency: Keeping Data in Synch in a Georeplicated World

TL;DR

This paper introduces fisheye consistency, a hybrid data consistency model for georeplicated systems that combines different consistency levels based on node proximity, supported by a distributed algorithm.

Contribution

It proposes a proximity graph-based framework for hybrid consistency conditions, including a new model called fisheye consistency, with a provably correct distributed algorithm.

Findings

The proximity graph enables tailored consistency levels for different node pairs.

The fisheye consistency model combines causal and sequential consistency.

A distributed algorithm implementing fisheye consistency is proven correct.

Abstract

Over the last thirty years, numerous consistency conditions for replicated data have been proposed and implemented. Popular examples of such conditions include linearizability (or atomicity), sequential consistency, causal consistency, and eventual consistency. These consistency conditions are usually defined independently from the computing entities (nodes) that manipulate the replicated data; i.e., they do not take into account how computing entities might be linked to one another, or geographically distributed. To address this lack, as a first contribution, this paper introduces the notion of proximity graph between computing nodes. If two nodes are connected in this graph, their operations must satisfy a strong consistency condition, while the operations invoked by other nodes are allowed to satisfy a weaker condition. The second contribution is the use of such a graph to provide a generic approach to the hybridization of data consistency conditions into the same system. We illustrate this approach on sequential consistency and causal consistency, and present a model in which all data operations are causally consistent, while operations by neighboring processes in the proximity graph are sequentially consistent. The third contribution of the paper is the design and the proof of a distributed algorithm based on this proximity graph, which combines sequential consistency and causal consistency (the resulting condition is called fisheye consistency). In doing so the paper not only extends the domain of consistency conditions, but provides a generic provably correct solution of direct relevance to modern georeplicated systems.