A Comparative Analysis of Generative Adversarial Networks for Generating Cloud Workloads

While Generative Adversarial Networks (GANs) have been highly successful in areas such as image generation, their efficacy in generating time series data, specifically for cloud workload applications, is not yet very well-established. Several GAN architectures have been proposed for time series generation, however there is a lack of comprehensive comparative analysis among these models for different real-world datasets in cloud workload domain. Additionally, prior research has not thoroughly explored the performance of models in relation to dataset attributes, including length of the data sequences, their seasonality and stationarity. This paper bridges this gap by focusing on cloud work-load time series data. We compare TimeGAN, RGAN, TTS- GAN, and V-GAN architectures using three real-world trace datasets-Alibaba 2017, Alibaba 2018, and Azure-to evaluate their performance when applied to these datasets with diverse characteristics. We intend this study to be an empirical guide for practitioners and researchers to choose the most appropriate GAN model based on the unique characteristics of their time series data. In this paper, we introduced a way to employ existing statistical measures to preprocess and characterize the datasets from varying standpoints. Then we used these datasets to assess the quality of these models' outputs qualitatively and quantitatively with respect to diversity, fidelity, and usability, for each kind of the input data. Our findings revealed the capabilities and limitations of each model, with regards to data characteristics such as sequence length, seasonality and stationarity. Based on our results, TimeGAN and TTS-GAN emerged as top-performing models in general across different datasets and sequence lengths. TimeGAN showed superiority with capturing short term tem-poral dynamics, while TTS-GAN outperformed in capturing long term dependencies. The transformer-based architecture employed in TTS-GAN makes it adept for handling highly seasonal data across both short and long sequence lengths. Conversely, TimeGAN demonstrated superior performance in accurately capturing highly seasonal data over shorter periods.

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