Causal broadcasting and consistency of distributed shared data (original) (raw)
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The problem of ensuring consistency in applications that manage replicated data is one of the main challenges of distributed computing. Among the several invariants that may be enforced, ensuring that updates are applied and made visible respecting causality has emerged as a key ingredient among the many consistency criteria and client session guarantees that have been proposed and implemented in the last decade. Techniques to keep track of causal dependencies, and to subsequently ensure that messages are delivered in causal order, have been widely studied. It is today well known that, in order to accurately capture causality one may need to keep a large amounts of metadata, for instance, one vector clock for each data object. This metadata needs to be updated and piggybacked on update messages, such that updates that are received from remote datacenters can be applied locally without violating causality. This metadata can be compressed; ultimately, it is possible to preserve causal...
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Several applications of distributed systems need to be highly scalable. For achieving high availability and fault tolerance, such applications typically replicate their data. Due to the dynamics of growth and volatility of the customer markets of such applications, they need to be hosted by highly scalable and adaptive systems. In particular, the scalability of the reliable broadcast mechanisms used for supporting the consistency of replicas is of crucial importance. Reliable broadcasts propagate updates in a predetermined 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 broadcast scalability, although the consistency guarantees based on causal order are weaker than those of total order. This paper provides a historical survey of different approaches used in reliable causal broadcast protocols. In particular, we focus on protocol mechanisms for enhancing both scalability and adaptability.
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