Article
Measuring data‑centre workfows complexity through process mining: the Google cluster case
Author/s | Fernández Cerero, Damián
Varela Vaca, Ángel Jesús Fernández Montes González, Alejandro Gómez López, María Teresa Álvarez Bermejo, José Antonio |
Department | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Publication Date | 2020 |
Deposit Date | 2022-03-14 |
Published in |
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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 ... 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 |
Funding agencies | Ministerio de Ciencia Y Tecnología (MCYT). España Universidad de Sevilla |
Project ID. | RTI2018-094283-B-C33
RTI2018-098062-A-I00 2018/00000520 |
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. |
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