Wasserstein Adversarial Transformer for Cloud Workload Prediction

• URL: http://arxiv.org/abs/2203.06501v1
• Date: Sat, 12 Mar 2022 18:58:00 GMT
• Title: Wasserstein Adversarial Transformer for Cloud Workload Prediction
• Authors: Shivani Arbat, Vinodh Kumaran Jayakumar, Jaewoo Lee, Wei Wang, In Kee Kim
• Abstract summary: This work presents a novel time-series forecasting model called WGAN-gp Transformer, inspired by the Transformer network and improved Wasserstein-GANs. The proposed method adopts a Transformer network as a generator and a multi-layer perceptron as a critic. We show WGAN-gp Transformer achieves 5 times faster inference time with up to 5.1 percent higher prediction accuracy against the state-of-the-art approach.
• Score: 5.4168067277885825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predictive Virtual Machine (VM) auto-scaling is a promising technique to optimize cloud applications operating costs and performance. Understanding the job arrival rate is crucial for accurately predicting future changes in cloud workloads and proactively provisioning and de-provisioning VMs for hosting the applications. However, developing a model that accurately predicts cloud workload changes is extremely challenging due to the dynamic nature of cloud workloads. Long-Short-Term-Memory (LSTM) models have been developed for cloud workload prediction. Unfortunately, the state-of-the-art LSTM model leverages recurrences to predict, which naturally adds complexity and increases the inference overhead as input sequences grow longer. To develop a cloud workload prediction model with high accuracy and low inference overhead, this work presents a novel time-series forecasting model called WGAN-gp Transformer, inspired by the Transformer network and improved Wasserstein-GANs. The proposed method adopts a Transformer network as a generator and a multi-layer perceptron as a critic. The extensive evaluations with real-world workload traces show WGAN-gp Transformer achieves 5 times faster inference time with up to 5.1 percent higher prediction accuracy against the state-of-the-art approach. We also apply WGAN-gp Transformer to auto-scaling mechanisms on Google cloud platforms, and the WGAN-gp Transformer-based auto-scaling mechanism outperforms the LSTM-based mechanism by significantly reducing VM over-provisioning and under-provisioning rates.

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