Evaluation of synthetically generated traces towards a data-centre digital twin
- Alejandro Fernandez-Montes
- Damian Fernandez Cerero
- Agnieszka Jakobik
- Belen Bermejo
- Carlos Juiz
(2023). ECMS 2023, 37th Proceedings
Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation.
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-79-1 (CD) Communications of the ECMS Volume 37, Issue 1, June 2023, Florence, Italy June 20th – June 23rd, 2023
Alejandro fernandez-montes, Damian fernandez cerero, Agnieszka jakobik, Belen bermejo, Carlos juiz (2023). Evaluation of synthetically generated traces towards a data-centre digital twin, ECMS 2023, Proceedings Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation. doi:10.7148/2023-0528
Several approaches exist to generate synthetic data centre traces for various purposes: from augmenting operating traces for data centre simulators and digital twins to forecasting the incoming workload to improve the data centre behaviour.
The evaluation of the quality of synthetically generated multivariate time-series datasets, such as those related to data-centre traces, is not a trivial task, since complex patterns and correlation between variables may be present.
This paper proposes a new multivariate time-series evaluation framework that computes a set of metrics and figures that can be used to measure the quality of synthetically generated data-centre traces. We then employ the proposed tool to compare two synthetic data centre traces with the original trace and assess their quality. These synthetic traces have been generated by means of Generative Adversarial Networks (GAN). In this work, we employ TimeGAN, a GAN model focused on the generation of multivariate time series traces.
We finally show how the proposed framework provides us with a set of metrics consistent with the observable behaviour and numerical insights on the quality of the generated data centre traces, which are hard to acquire otherwise.