# Wasserstein Adversarial Transformer for Cloud Workload Prediction

arXiv: https://arxiv.org/abs/2203.06501 · HTML: https://mlsanity.com/paper/2203.06501/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel](https://mlsanity.com/paper/1908.11775/) (2019)
- [TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up](https://mlsanity.com/paper/2102.07074/) (2021)
- [Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization](https://mlsanity.com/paper/2101.11075/) (2021)
- [An Empirical Analysis of VM Startup Times in Public IaaS Clouds: An Extended Report](https://mlsanity.com/paper/2107.03467/) (2021)

## Built on by (most cited first, 4 of 4)

- [One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms](https://mlsanity.com/paper/2306.01507/) (2023)
- [Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Transfer Learning](https://mlsanity.com/paper/2303.13525/) (2023)
- [Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scaling](https://mlsanity.com/paper/2508.12773/) (2025)
- [HRS: Hybrid Representation Framework with Scheduling Awareness for Time Series Forecasting in Crowdsourced Cloud-Edge Platforms](https://mlsanity.com/paper/2508.12839/) (2025)

## Abstract (arXiv)

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.