<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nuno Preguiça</style></author><author><style face="normal" font="default" size="100%">Carlos Baquero Moreno</style></author><author><style face="normal" font="default" size="100%">Paulo Sérgio Almeida</style></author><author><style face="normal" font="default" size="100%">Vitor Fonte</style></author><author><style face="normal" font="default" size="100%">Ricardo Gonçalves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dotted Version Vectors: Logical Clocks for Optimistic Replication</style></title><secondary-title><style face="normal" font="default" size="100%">CoRR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/26</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://haslab.uminho.pt/sites/default/files/psa/files/dvv-arxiv.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">HASLab/INESC TEC &amp; University of Minho</style></publisher><pub-location><style face="normal" font="default" size="100%">Braga, Portugal</style></pub-location><volume><style face="normal" font="default" size="100%">1011.5808</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In cloud computing environments, a large number of users access data stored in highly available storage systems. To provide good performance to geographically disperse users and allow operation even in the presence of failures or network partitions, these systems often rely on optimistic replication solutions that guarantee only eventual consistency. In this scenario, it is important to be able to accurately and efficiently identify updates executed concurrently. In this paper, first we review, and expose problems with current approaches to causality tracking in optimistic replication: these either lose information about causality or do not scale, as they require replicas to maintain information that grows linearly with the number of clients or updates.Then, we propose a novel solution that fully captures causality while being very concise in that it maintains information that grows linearly only with the number of servers that register updates for a given data element, bounded by the degree of replication.&lt;/p&gt;
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