CATS: linearizability and partition tolerance in scalable and self-organizing key-value stores

Distributed key-value stores provide scalable, fault-tolerant, and self-organizing storage services, but fall short of guaranteeing linearizable consistency in partially synchronous, lossy, partitionable, and dynamic networks, when data is distributed and replicated automatically by the principle of consistent hashing. This paper introduces consistent quorums as a solution for achieving atomic consistency. We present the design and implementation of CATS, a distributed key-value store which uses consistent quorums to guarantee linearizability and partition tolerance in such adverse and dynamic network conditions. CATS is scalable, elastic, and self-organizing; key properties for modern cloud storage middleware. Our system shows that consistency can be achieved with practical performance and modest throughput overhead (5%) for read-intensive workloads

Tunable Causal Consistency: Specification and Implementation

To achieve high availability and low latency, distributed data stores often geographically replicate data at multiple sites called replicas. However, this introduces the data consistency problem. Due to the fundamental tradeoffs among consistency, availability, and latency in the presence of network partition, no a one-size-fits-all consistency model exists. To meet the needs of different applications, many popular data stores provide tunable consistency, allowing clients to specify the consistency level per individual operation. In this paper, we propose tunable causal consistency (TCC). It allows clients to choose the desired session guarantee for each operation, from the well-known four session guarantees, i.e., read your writes, monotonic reads, monotonic writes, and writes follow reads. Specifically, we first propose a formal specification of TCC in an extended (vis,ar) framework originally proposed by Burckhardt et al. Then we design a TCC protocol and develop a prototype distributed key-value store called TCCSTORE. We evaluate TCCSTORE on Aliyun. The latency is less than 38ms for all workloads and the throughput is up to about 2800 operations per second. We also show that TCC achieves better performance than causal consistency and requires a negligible overhead when compared with eventual consistency

On the Behaviour of General-Purpose Applications on Cloud Storages

Managing data over cloud infrastructures raises novel challenges with respect to existing and well studied approaches such as ACID and long running transactions. One of the main requirements is to provide availability and partition tolerance in a scenario with replicas and distributed control. This comes at the price of a weaker consistency, usually called eventual consistency. These weak memory models have proved to be suitable in a number of scenarios, such as the analysis of large data with Map-Reduce. However, due to the widespread availability of cloud infrastructures, weak storages are used not only by specialised applications but also by general purpose applications. We provide a formal approach, based on process calculi, to reason about the behaviour of programs that rely on cloud stores. For instance, one can check that the composition of a process with a cloud store ensures `strong' properties through a wise usage of asynchronous message-passing