<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">João Paulo</style></author><author><style face="normal" font="default" size="100%">José Orlando Pereira</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model Checking a Decentralized Storage Deduplication Protocol</style></title><secondary-title><style face="normal" font="default" size="100%">Fast Abstract in Latin-American Symposium on Dependable Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">April</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://haslab.uminho.pt/sites/default/files/jtpaulo/files/pp09.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">São José dos Campos, Brazil</style></pub-location><pages><style face="normal" font="default" size="100%">1</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Deduplication of live storage volumes in a cloud computing environment is better done by post-processing: by delaying discovery and removal of duplicate data after I/O requests have been concluded, impact in latency can be minimized. When compared to traditional deduplication in backup systems, which can be done in-line and in a centralized fashion, distribution and concurrency lead to increased complexity. This paper outlines a deduplication algorithm for a typical cloud infrastructure with a common storage pool and summarizes how model-checking with the TLA+ toolset was used to uncover and correct some subtle concurrency issues.&lt;/p&gt;
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