Cure: Strong Semantics Meets High Availability and Low Latency

Akkoorath, Deepthi Devaki; Tomsic, Alejandro Z.; Bravo, Manuel; Li, Zhongmiao; Crain, Tyler; Bieniusa, Annette; Preguiça, Nuno; Shapiro, Marc

doi:10.1109/icdcs.2016.98

Cited by 85 publications

(116 citation statements)

References 15 publications

(1 reference statement)

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“…As expected, existing causally consistent storage systems achieve higher performance in comparison to strong consistency systems [3,25,38,39]. However, causally consistent read-only transactions still suffer from latency overheads.…”

Section: Introductionmentioning

confidence: 65%

“…However, causally consistent read-only transactions still suffer from latency overheads. In fact, state-of-the-art causally consistent storage systems either do not support fast read-only transactions [3,25,42,54] (i.e., they do not exhibit all three desirable properties) or they are of restricted functionality by not providing multi-object write transactions [40]. Contributions.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Transactional Systems Cannot Be Fast

Didona

Fatourou

Guerraoui

et al. 2019

The 31st ACM Symposium on Parallelism in Algorithms and Architectures

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We prove that no fully transactional system can provide fast read transactions (including read-only ones that are considered the most frequent in practice). Specifically, to achieve fast read transactions, the system has to give up support of transactions that write more than one object. We prove this impossibility result for distributed storage systems that are causally consistent, i.e., they do not require to ensure any strong form of consistency. Therefore, our result holds also for any system that ensures a consistency level stronger than causal consistency, e.g., strict serializability. The impossibility result holds even for systems that store only two objects (and support at least two servers and at least four clients). It also holds for systems that are partially replicated. Our result justifies the design choices of state-of-the-art distributed transactional systems and insists that system designers should not put more effort to design fullyfunctional systems that support both fast read transactions and ensure causal or any stronger form of consistency.

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Section: Introductionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Distributed Transactional Systems Cannot Be Fast

Didona

Fatourou

Guerraoui

et al. 2019

The 31st ACM Symposium on Parallelism in Algorithms and Architectures

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show abstract

“…In GentleRain [13], for example, this is because a full DC failure prevents nodes in other DCs to install remote updates. In Cure [15], instead, a recovery mechanism is needed because healthy DCs might have not received the same set of updates from the failed DC, leading their states to diverge.…”

Section: B Data Freshness Vs Availabilitymentioning

confidence: 99%

“…Because POCC is based on physical vector clocks, POCC can be easily augmented to fall-back to run the protocol implemented in Cure [15], a recent causally consistent system also based on physical vector clocks 2 . We call Highly Available POCC (HA-POCC) the resulting augmented version of POCC.…”

Section: Highly Available Poccmentioning

confidence: 99%

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Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

Spirovska

Didona

Zwaenepoel

2017

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)

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Abstract-Causal consistency is an attractive consistency model for geo-replicated data stores because it hits a sweet spot in the ease of programmability vs performance trade-off.In this paper we propose a new approach to causal consistency, which we call Optimistic Causal Consistency (OCC). The optimism of our approach lies in the fact that updates from a remote data center are immediately made visible to clients in the local data center. A client, hence, always reads the freshest version of an item, whose dependencies, however, might have not been installed in the local data center yet. When serving a read request, a server can detect whether it has not received such dependencies yet. This is achieved without inter-server synchronization thanks to cheap dependency meta-data supplied by the client. Upon detecting a missing dependency, the server waits to receive it.This approach contrasts with the design of existing systems, which are prone to expose stale versions of a data items, to ensure that clients only see versions whose dependencies have already been replicated in the local data center.OCC explores a novel trade-off in the landscape of consistency models. Because network partitions are practically rare events, OCC partially trades availability to improve other performance metrics. On the one side, OCC maximizes the freshness of data returned to clients and reduces the communication overhead. On the other side, a server might need to wait before serving a client's request, leading the system to be unavailable in case of a network partition. To overcome this limitation, we propose a recovery mechanism that allows an OCC system to fall back to a pessimistic protocol to recover availability.We implement OCC in a new system, which we call POCC. We compare POCC against a recent (pessimistic) approach to causal consistency using heterogeneous workloads on an Amazon AWS deployment encompassing up to 96 nodes scattered over 3 data centers. We show that POCC is able to maximize the freshness of data returned to client while providing comparable or better performance than its pessimistic counterpart in a wide range of production-like workloads.

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Adjoin: A causal consistency model based on the adjacency list in a distributed system

Tian

Pang

2020

Concurrency and Computation

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Data consistency is a critical topic in distributed systems. In existing consistency models, causal consistency has attracted a significant amount of attention because it can satisfy high-performance requirements even in the presence of network partitions. At present, most of the causal consistency models face a tradeoff between throughput and update visibility. Simultaneously, they cannot take full advantage of partial geo-replication. To resolve the problems, this paper proposes a causal consistency model that supports partial replication using the adjacency list, called Adjoin. In Adjoin, each data center (DC) stores only a subset of the full data, by reading adjacency relationships, and the relevant nodes quickly reach synchronization. We also introduce the Adjacency Stable Vector and Adjacency Dependency Set to capture causality, which reduces the system storage overhead. We evaluate Adjoin with different workloads on a cloud platform using multiple sites. The results show that Adjoin has good performance in terms of throughput and update visibility compared with previous causal consistency models.

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Cure: Strong Semantics Meets High Availability and Low Latency

Cited by 85 publications

References 15 publications

Distributed Transactional Systems Cannot Be Fast

Distributed Transactional Systems Cannot Be Fast

Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

Adjoin: A causal consistency model based on the adjacency list in a distributed system

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