Scalable and Accurate Causality Tracking for Eventually Consistent Stores

Nuno Manuel Ribeiro Preguiça

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Pages67-81
ISBN (Print)978-3-662-43351-5
DOIs
Publication statusPublished - 2014
Event14th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems (DAIS) -
Duration: 1 Jan 2014 → …

Conference

Conference14th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems (DAIS)
Period1/01/14 → …

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