Abstract
Eventual consistency is a relaxed consistency model used in large-scale distributed systems that seek better availability when consistency can be delayed. CRDTs are distributed data types that make eventual consistency of a distributed object possible and non ad-hoc. Specifically, state-based CRDTs achieve this through shipping the entire replica state that is, eventually, merged to other replicas ensuring conver- gence. This imposes a large communication overhead when the replica size or the number of replicas gets larger. In this work, we introduce a decomposable version of state-based CRDTs, called Delta State-based CRDTs (d-CRDT). A d-CRDT is viewed as a join of multiple fine-grained CRDTs of the same type, called deltas (d). The deltas are produced by applying d-mutators, on a replica state, which are mod- ified versions of the original CRDT mutators. This makes it possible to ship small deltas (or batches) instead of ship- ping the entire state. The challenges are to make the join of deltas equivalent to the join of the entire object in clas- sical state-based CRDTs, and to find a way to derive the d-mutators. We address this challenge in this work, and we explore the minimal requirements that a communication al- gorithm must offer according to the guarantees provided by the underlying messaging middleware. Copyright © 2007 by the Association for Computing Machinery, Inc.

Abstract
Conict-free Replicated Datatypes can simplify the design of predictable eventual consistency. They can be classified into state-based or operation-based. Operation-based ap- proaches have the potential for allowing compact designs in both the sent message and the object state size, but cur- rent approaches are still far from this objective. Here we explore the design space for operation-based solutions, and we leverage the interaction with the middleware by offering a technique that delivers very compact solutions, while only broadcasting operation names and arguments. Copyright © 2007 by the Association for Computing Machinery, Inc.

Abstract
Conflict-free Replicated Datatypes (CRDT) are usually classified as either state-based or operation-based. However, the standard definition of op-based CRDTs is very encompassing, allowing even sending the full-state, blurring the distinction. We introduce pure op-based CRDTs, that can only send operations to other replicas, drawing a clear distinction from state-based ones. Datatypes with commutative operations can be trivially implemented as pure op-based CRDTs using standard reliable causal delivery. We propose an extended API - tagged reliable causal broadcast - that provides causality information upon delivery, and show how it can be used to also implement other datatypes having non-commutative operations, through the use of a PO-Log - a partially ordered log of operations - inside the datatype. A semanticallybased PO-Log compaction framework, using both causality and what we denote by causal stability, allows obtaining very compact replica state for pure op-based CRDTs, while also benefiting from small message sizes.

Abstract
In cloud computing environments, data storage systems often rely on optimistic replication to provide good performance and availability even in the presence of failures or network partitions. In this scenario, it is important to be able to accurately and efficiently identify updates executed concurrently. Current approaches to causality tracking in optimistic replication have problems with concurrent updates: they either (1) do not scale, as they require replicas to maintain information that grows linearly with the number of writes or unique clients; (2) lose information about causality, either by removing entries from client-id based version vectors or using server-id based version vectors, which cause false conflicts. We propose a new logical clock mechanism and a logical clock framework that together support a traditional key-value store API, while capturing causality in an accurate and scalable way, avoiding false conflicts. It maintains concise information per data replica, only linear on the number of replica servers, and allows data replicas to be compared and merged linear with the number of replica servers and versions.

Abstract
Client-side logic and storage are increasingly used in web and mobile applications to improve response time and availability. Current approaches tend to be ad-hoc and poorly integrated with the server-side logic. We present a principled approach to integrate client-and server-side storage. We support both mergeable and strongly consistent transactions that target either client or server replicas and provide access to causally-consistent snapshots efficiently. In the presence of infrastructure faults, a client-assisted failover solution allows client execution to resume immediately and seamlessly access consistent snapshots without waiting. We implement this approach in SwiftCloud, the first transactional system to bring geo-replication all the way to the client machine. Example applications show that our programming model is useful across a range of application areas. Our experimental evaluation shows that SwiftCloud provides better fault tolerance and at the same time can improve both latency and throughput by up to an order of magnitude, compared to classical geo-replication techniques. © 2014 IEEE.

Abstract
The information infrastructure that pervades urban environments represents a major opportunity for collecting information about Human mobility. However, this huge potential has been undermined by the overwhelming privacy risks that are associated with such forms of large scale sensing. In this research, we are concerned with the problem of how to enable a set of autonomous sensing nodes, e.g. a Bluetooth scanner or a Wi-Fi hotspot, to collaborate in the observation of movement patterns of individuals without compromising their privacy. We describe a novel technique that generates Precedence Filters and allows probabilistic estimations of sequences of visits to monitored locations and we demonstrate how this technique can combine plausible deniability by an individual with valuable information about aggregate movement patterns.