Abstract

Abstract
CRDTs are distributed data types that make eventual consistency of a distributed object possible and non ad-hoc. Specifically, state-based CRDTs ensure convergence through disseminating the entire state, that may be large, and merging it to other replicas; whereas operation-based CRDTs disseminate operations (i.e., small states) assuming an exactly-once reliable dissemination layer. We introduce Delta State Conflict-Free Replicated Datatypes (d-CRDT) that can achieve the best of both worlds: small messages with an incremental nature, disseminated over unreliable communication channels. This is achieved by defining d-mutators to return a delta-state, typically with a much smaller size than the full state, that is joined to both: local and remote states. We introduce the d-CRDT framework, and we explain it through establishing a correspondence to current state-based CRDTs. In addition, we present an anti-entropy algorithm that ensures causal consistency, and two d-CRDT specifications of well-known replicated datatypes. © Springer International Publishing Switzerland 2015.

Abstract
Many state-machine Byzantine Fault Tolerant (BFT) protocols have been introduced so far. Each protocol addressed a different subset of conditions and use-cases. However, if the underlying conditions of a service span different subsets, choosing a single protocol will likely not be a best fit. This yields robustness and performance issues which may be even worse in services that exhibit fluctuating conditions and workloads. In this paper, we reconcile existing state-machine BFT protocols in a single adaptive BFT system, called ADAPT, aiming at covering a larger set of conditions and use-cases, probably the union of individual subsets of these protocols. At anytime, a launched protocol in ADAPT can be aborted and replaced by another protocol according to a potential change (an event) in the underlying system conditions. The launched protocol is chosen according to an 'evaluation process' that takes into consideration both: protocol characteristics and its performance. This is achieved by applying some mathematical formulas that match the profiles of protocols to given user (e.g., service owner) preferences. ADAPT can assess the profiles of protocols (e.g., throughput) at run-time using Machine Learning prediction mechanisms to get accurate evaluations. We compare ADAPT with well known BFT protocols showing that it outperforms others as system conditions change and under dynamic workloads. © 2015 IEEE.