Logical time: capturing causality in distributed systems

Raynal, Michel; Singhal, Mukesh

doi:10.1109/2.485846

Cited by 162 publications

(103 citation statements)

References 20 publications

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“…The role of causality in distributed systems was introduced by Lamport [11], establishing the foundation for the subsequent mechanisms and theory [11,14,20,19,2,4]. In Section 3 we discussed the problems of solutions commonly used in eventually consistent stores.…”

Section: Related Workmentioning

confidence: 99%

“…Accurate tracking of concurrent data updates can be achieved by a careful use of well established causality tracking mechanisms [11,14,20,19,2]. In particular, for data storage systems, version vectors (vv) [14] enable the system to compare any pair of replica versions and detect if they are equivalent, concurrent or if one makes the other obsolete.…”

Section: Introductionmentioning

confidence: 99%

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Scalable and Accurate Causality Tracking for Eventually Consistent Stores

Almeida

Baquero

Gonçalves

et al. 2014

Distributed Applications and Interoperable Systems

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scalable and Accurate Causality Tracking for Eventually Consistent Stores

Almeida

Baquero

Gonçalves

et al. 2014

Distributed Applications and Interoperable Systems

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“…Since the Lamport's seminal paper [5], that formalized the notion of causal dependency between events of an asynchronous distributed computation, a lot of work has been carried out to design distributed algorithms that capture the causal dependencies (or the concurrency) between events during a computation [7]. All these algorithms are based on timestamps associated with events and on the piggybacking of information on messages used to update timestamps.…”

Section: Introductionmentioning

confidence: 99%

Timestamping Algorithms: A Characterization and a Few Properties

Melideo

Mechelli

Baldoni

et al. 2000

Euro-Par 2000 Parallel Processing

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Abstract. Timestamping algorithms are used to capture the causal order or the concurrency of events in asynchronous distributed computations. This paper introduces a formal framework on timestamping algorithms, by characterizing some conditions they have to satisfy in order to capture causality. Under the proposed formal framework we derive a few properties about the size of timestamps and local informations at processes obtained by counting the number of distinct causal pasts which could be observed by an omniscient observer during the evolution of a distributed computation.

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“…Chetlur and Wilsey addressed this issue and proposed the range based cancellation scheme [2] by taking advantage of vector time [12,10], a well-understood mechanism to capture the happen-before relation (denoted as →) [7] between events in distributed systems. A new type of message, namely, CANCEL MESSAGE, was introduced as a replacement of anti-messages in Time Warp simulations.…”

Section: Introductionmentioning

confidence: 99%

Batch based cancellation: a rollback optimal cancellation scheme in time warp simulations

Zeng¹,

Cai²,

Turner³

18th Workshop on Parallel and Distributed Simulation, 2004. PADS 2004.

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An efficient cancellation scheme is essential to the performance of Time Warp simulations. The pitfalls of rollback echoes, chasing hazards and cascading rollbacks can be identified as being attributable to the inefficiency of the conventional per-event based cancellation scheme. Instead of capturing the happen-before relation between events, which is used by the range based cancellation scheme, the batch based cancellation scheme proposed in this paper utilizes a modified paradigm of vector time, namely, state vector, to capture the dependence of events. We prove that with conformance to specific rules regulating the advancement of LPs, the events to be cancelled by a straggler message can be determined using a range of the state vector. Thus, knowledge of the range enables any LP to recover from the receipt of a straggler message at the cost of at most one rollback (i.e., rollback optimal). The results of preliminary experiments conducted using a manufacturing model show that the proposed scheme is successful in reducing the number of anti-messages and increasing the ratio of the number of committed events to the number of processed events.

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Logical time: capturing causality in distributed systems

Cited by 162 publications

References 20 publications

Scalable and Accurate Causality Tracking for Eventually Consistent Stores

Scalable and Accurate Causality Tracking for Eventually Consistent Stores

Timestamping Algorithms: A Characterization and a Few Properties

Batch based cancellation: a rollback optimal cancellation scheme in time warp simulations

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