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.

本文提出了一种名为 WGAN-gp Transformer 的新型时间序列预测模型，它的生成器基于 Transformer 网络，评论家采用 MLP。该方法对真实工作负载跟踪进行了广泛的评估，发现其可达到 5 倍的快速推理时间和高达 5.1% 的预测准确率。同时还将 WGAN-gp Transformer 应用于 Google 云平台的自动缩放机制，发现其优于基于 LSTM 的机制。