Machine learning data center workloads using generative adversarial networks

In this paper we study the applicability of generative adversarial networks (GANs) for the description and generation of workloads for data centers. GANs are advanced neural networks that can learn complex likelihood functions and can sample from them. The field of workload modeling is concerned with describing and generating realistic workloads for performance evaluation of computer systems, in this paper, specifically for data centers. The characterization of the workload of modern data centers is crucial in order to study the effect of changing workloads on the performance of such data centers. Previously, a number of statistical fitting techniques have been used to characterize data center workloads. This paper explores whether GANs are sufficiently capable to automatically learn such characterisations from multidimensional data sets. We describe the design and evaluation of a GAN, thereby using real-world data center traces. The learned model is evaluated by comparison to previously proposed fitting techniques. We find that the resulting GAN is very well able to reproduce a realistic data center workload. Furthermore, the approach does not require (a priori) knowledge or assumptions about the underlying models themselves, which can be seen as an advantage. It is shown that the learning approach does reach comparable quality to other fitting techniques, although still at much higher computational costs.