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dc.contributor.authorXie, Zhiwu
dc.contributor.authorChen, Yinlin
dc.contributor.authorSpeer, Julie
dc.contributor.authorWalters, Tyler
dc.date.accessioned2016-06-28T20:32:21Z
dc.date.available2016-06-28T20:32:21Z
dc.date.issued2016-06
dc.identifier.urihttp://hdl.handle.net/10919/71647
dc.description.abstractIn this paper, we utilize a set of controlled experiments to benchmark the cost associated with the cloud execution of typical repository functions such as ingestion, fixity checking, and heavy data processing. We focus on the repository service pattern where content is explicitly stored away from where it is processed. We measured the processing speed and unit cost of each scenario using a large sensor dataset and Amazon Web Services (AWS). The initial results reveal three distinct cost patterns: 1) spend more to buy up to proportionally faster services; 2) more money does not necessarily buy better performance; and 3) spend less, but faster. Further investigations into these performance and cost patterns will help repositories to form a more effective operation strategy.en_US
dc.language.isoen_USen_US
dc.publisherACMen_US
dc.relation.ispartof16th ACM/IEEE-CS Joint Conference on Digital Librariesen_US
dc.rightsAttribution-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/3.0/us/*
dc.subjectInstitutional repositoryen_US
dc.subjectBig dataen_US
dc.subjectCloud computingen_US
dc.subjectCost analysisen_US
dc.titleEvaluating Cost of Cloud Execution in a Data Repositoryen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1145/2910896.2925454


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Attribution-ShareAlike 3.0 United States
License: Attribution-ShareAlike 3.0 United States