dc.contributor.author	Xie, Zhiwu	en
dc.contributor.author	Chen, Yinlin	en
dc.contributor.author	Griffin, Julie	en
dc.contributor.author	Walters, Tyler	en
dc.date.accessioned	2016-06-28T20:32:21Z	en
dc.date.available	2016-06-28T20:32:21Z	en
dc.date.issued	2016-06	en
dc.description.abstract	In 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
dc.identifier.doi	https://doi.org/10.1145/2910896.2925454	en
dc.identifier.uri	http://hdl.handle.net/10919/71647	en
dc.language.iso	en_US	en
dc.publisher	ACM	en
dc.relation.ispartof	16th ACM/IEEE-CS Joint Conference on Digital Libraries	en
dc.rights	Creative Commons Attribution-ShareAlike 3.0 United States	en
dc.rights.uri	http://creativecommons.org/licenses/by-sa/3.0/us/	en
dc.subject	Institutional repository	en
dc.subject	Big data	en
dc.subject	Cloud computing	en
dc.subject	Cost analysis	en
dc.title	Evaluating Cost of Cloud Execution in a Data Repository	en
dc.type	Article	en

Evaluating Cost of Cloud Execution in a Data Repository

Files

Original bundle

License bundle

Collections