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Measuring data‑centre workfows complexity through process mining: the Google cluster case
dc.creator | Fernández Cerero, Damián | es |
dc.creator | Varela Vaca, Ángel Jesús | es |
dc.creator | Fernández Montes González, Alejandro | es |
dc.creator | Gómez López, María Teresa | es |
dc.creator | Álvarez Bermejo, José Antonio | es |
dc.date.accessioned | 2022-03-14T08:49:49Z | |
dc.date.available | 2022-03-14T08:49:49Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Fernández Cerero, D., Varela Vaca, Á.J., Fernández Montes González, A., Gómez López, M.T. y Álvarez Bermejo, J.A. (2020). Measuring data‑centre workfows complexity through process mining: the Google cluster case. The Journal of Supercomputing, 76 (4), 2449-2478. | |
dc.identifier.issn | 0920-8542 | es |
dc.identifier.uri | https://hdl.handle.net/11441/130735 | |
dc.description.abstract | Data centres have become the backbone of large Cloud services and applica-tions, providing virtually unlimited elastic and scalable computational and storage resources. The search for the efficiency and optimisation of resources is one of the current key aspects for large Cloud Service Providers and is becoming more and more challenging, since new computing paradigms such as Internet of Things, Cyber-Physical Systems and Edge Computing are spreading. One of the key aspects to achieve efficiency in data centres consists of the discovery and proper analysis of the data-centre behaviour. In this paper, we present a model to automatically retrieve execution workflows of existing data-centre logs by employing process mining tech-niques. The discovered processes are characterised and analysed according to the understandability and complexity in terms of execution efficiency of data-centre jobs. We finally validate and demonstrate the usability of the proposal by applying the model in a real scenario, that is, the Google Cluster traces | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología RTI2018–094283-B-C33 | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología RTI2018-098062-A-I00 | es |
dc.description.sponsorship | Universidad de Sevilla 2018/00000520 | es |
dc.format | application/pdf | es |
dc.format.extent | 30 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | The Journal of Supercomputing, 76 (4), 2449-2478. | |
dc.rights | An error occurred on the license name. | * |
dc.rights.uri | An error occurred getting the license - uri. | * |
dc.subject | Cloud computing | es |
dc.subject | Business process management | es |
dc.subject | Scheduling | es |
dc.subject | Process mining | es |
dc.subject | Process discovery | es |
dc.subject | High performance computing | es |
dc.title | Measuring data‑centre workfows complexity through process mining: the Google cluster case | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | RTI2018-094283-B-C33 | es |
dc.relation.projectID | RTI2018-098062-A-I00 | es |
dc.relation.projectID | 2018/00000520 | es |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s11227-019-02996-2 | es |
dc.identifier.doi | 10.1007/s11227-019-02996-2 | es |
dc.contributor.group | Universidad de Sevilla. TIC134: Sistemas Informáticos | es |
dc.journaltitle | The Journal of Supercomputing | es |
dc.publication.volumen | 76 | es |
dc.publication.issue | 4 | es |
dc.publication.initialPage | 2449 | es |
dc.publication.endPage | 2478 | es |
dc.contributor.funder | Ministerio de Ciencia Y Tecnología (MCYT). España | es |
dc.contributor.funder | Universidad de Sevilla | es |
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