Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks

• URL: http://arxiv.org/abs/2201.06147v1
• Date: Wed, 12 Jan 2022 15:09:10 GMT
• Title: Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks
• Authors: Jaime P\'erez, Patricia Arroba and Jos\'e M. Moya
• Abstract summary: The main challenge in achieving a global energy efficiency strategy based on Artificial Intelligence is that we need massive amounts of data to feed the algorithms. This paper proposes a time-series data augmentation methodology based on synthetic scenario forecasting within the Data Center. Our research will help to optimize the energy consumed in Data Centers, although the proposed methodology can be employed in any similar time-series-like problem.
• Score: 0.18416014644193063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Cloud paradigm is at a critical point in which the existing energy-efficiency techniques are reaching a plateau, while the computing resources demand at Data Center facilities continues to increase exponentially. The main challenge in achieving a global energy efficiency strategy based on Artificial Intelligence is that we need massive amounts of data to feed the algorithms. Nowadays, any optimization strategy must begin with data. However, companies with access to these large amounts of data decide not to share them because it could compromise their security. This paper proposes a time-series data augmentation methodology based on synthetic scenario forecasting within the Data Center. For this purpose, we will implement a powerful generative algorithm: Generative Adversarial Networks (GANs). The use of GANs will allow us to handle multivariate data and data from different natures (e.g., categorical). On the other hand, adapting Data Centers' operational management to the occurrence of sporadic anomalies is complicated due to the reduced frequency of failures in the system. Therefore, we also propose a methodology to increase the generated data variability by introducing on-demand anomalies. We validated our approach using real data collected from an operating Data Center, successfully obtaining forecasts of random scenarios with several hours of prediction. Our research will help to optimize the energy consumed in Data Centers, although the proposed methodology can be employed in any similar time-series-like problem.

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