Scalability approaches for causal multicast: a survey

Abstract

Many distributed services need to be scalable: internet search, electronic commerce, e-government... In order to achieve scalability those applications rely on replicated components. Because of the dynamics of growth and volatility of customer markets, applications need to be hosted by adaptive systems. In particular, the scalability of the reliable multicast mechanisms used for supporting the consistency of replicas is of crucial importance. Reliable multicast may propagate updates in a pre-defined order (e.g., FIFO, total or causal). Since total order needs more communication rounds than causal order, the latter appears to be the preferable candidate for achieving multicast scalability, although the consistency guarantees based on causal order are weaker than those of total order. This paper provides a historical survey of different scalability approaches for reliable causal multicast protocols.

Putting Order in Strong Eventual Consistency

“…Similarly, we denote that two mutations happened concurrently using m 1 m 2 . Both relations are based on the clocks carried by the mutators [8].…”

CScript: A distributed programming language for building mixed-consistency applications

“…Similarly, we denote that two mutations happened concurrently using m 1 m 2 . Both relations are based on the clocks carried by the mutate messages [22].…”

Efficient Synchronization of State-Based CRDTs

“…Operation-based: Operation-based CRDTs [7], [8] resort to a causal broadcast middleware [22] that is used to disseminate CRDT operations. This middleware tags each operation with a vector clock that summarizes the causal past of the operation.…”