dc.contributor.author | Smowton, C. | en |
dc.contributor.author | Balla, A. | en |
dc.contributor.author | Antoniades, Demetris | en |
dc.contributor.author | Miller, C. | en |
dc.contributor.author | Pallis, George C. | en |
dc.contributor.author | Dikaiakos, Marios D. | en |
dc.contributor.author | Xing, Wei | en |
dc.creator | Smowton, C. | en |
dc.creator | Balla, A. | en |
dc.creator | Antoniades, Demetris | en |
dc.creator | Miller, C. | en |
dc.creator | Pallis, George C. | en |
dc.creator | Dikaiakos, Marios D. | en |
dc.creator | Xing, Wei | en |
dc.date.accessioned | 2019-11-13T10:42:18Z | |
dc.date.available | 2019-11-13T10:42:18Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 0167-739X | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54988 | |
dc.description.abstract | With the rapidly growing demand for DNA analysis, the need for storing and processing large-scale genome data has presented significant challenges. This paper describes how the Genome Analysis Toolkit (GATK) can be deployed to an elastic cloud, and defines policy to drive elastic scaling of the application. We extensively analyse the GATK to expose opportunities for resource elasticity, demonstrate that it can be practically deployed at scale in a cloud environment, and demonstrate that applying elastic scaling improves the performance to cost tradeoff achieved in a simulated environment. © 2015 Elsevier B.V. | en |
dc.source | Future Generation Computer Systems | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950255455&doi=10.1016%2fj.future.2015.11.011&partnerID=40&md5=3c026d98f81f236421170ec4e4252ce6 | |
dc.subject | Performance | en |
dc.subject | Cost effectiveness | en |
dc.subject | Genes | en |
dc.subject | Cost benefit analysis | en |
dc.subject | Cost-effective approach | en |
dc.subject | Digital storage | en |
dc.subject | Improving performance | en |
dc.subject | Clouds | en |
dc.subject | Big data | en |
dc.subject | Cloud environments | en |
dc.subject | Cost trade-off | en |
dc.subject | Genome analysis | en |
dc.subject | Growing demand | en |
dc.subject | Simulated environment | en |
dc.title | A cost-effective approach to improving performance of big genomic data analyses in clouds | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1016/j.future.2015.11.011 | |
dc.description.volume | 67 | |
dc.description.startingpage | 368 | |
dc.description.endingpage | 381 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :1</p> | en |
dc.source.abbreviation | Future Gener Comput Syst | en |
dc.contributor.orcid | Pallis, George C. [0000-0003-1815-5468] | |
dc.contributor.orcid | Dikaiakos, Marios D. [0000-0002-4350-6058] | |
dc.gnosis.orcid | 0000-0003-1815-5468 | |
dc.gnosis.orcid | 0000-0002-4350-6058 | |