As current trends start to move storage and computation to the edge in order to provide support for latency constrained applications, new edge storage systems must emerge that optimise latency and reduce the cost of communication, so that they can provide users with the best possible experience. With this goal in mind, several new storage systems have surfaced that make the most of sophisticated replication techniques and weak consistency models, in particular the causal+ consistency model. A way to build such a system is to use a causal broadcast algorithm to propagate write operations between replicas in an order that is compatible with the causal order. However, existing systems rely on mechanisms that have limitations: they either leverage on static tree topologies, not adapting to scenarios where replicas join or leave the system, or they use metadata that grows linearly with the number of replicas, being therefore incapable of scaling to the hundreds or thousands of (smaller) replicas and, as such, not being suitable for supporting the operation of edge data stores. In this work, we propose a new edge-enabled replication scheme that ensures causal delivery of operations in all replicas and, when used together with CRDTs, guarantees causal+ consistency. Our solution is a decentralised causal broadcast algorithm, ECO SYNC Tree, that makes use of a dynamic tree topology, capable of quickly adapting to nodes joining and leaving the system, to offer causal delivery while using negligible metadata to encode causal dependencies.

We present an experimental evaluation of ECO SYNC Tree that shows that our solution captures “the best of both worlds” when it comes to the trade-off between broadcast latency and communication cost in stable environments, and in environments subject to events such as large groups of nodes joining or leaving the system. Moreover, when compared with state-of-the-art broadcast protocols, ECO SYNC Tree proved to be the one that is best suited for edge-based deployment, by exhibiting better performance and scalability in scenarios with high churn rates.