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。

云工作负载预测的 Wasserstein 对抗变换器

Wasserstein Adversarial Transformer for Cloud Workload Prediction

A fundamental challenge in large-scale cloud networks and data centers is to
achieve highly efficient server utilization and limit energy consumption, while
providing excellent user-perceived performance in the presence of uncertain and
time-varying demand patterns. Auto-scaling provides a popular paradigm for
automatically adjusting service capacity in response to demand while meeting
performance targets, and queue-driven auto-scaling techniques have been widely
investigated in the literature. In typical data center architectures and cloud
environments however, no centralized queue is maintained, and load balancing
algorithms immediately distribute incoming tasks among parallel queues. In
these distributed settings with vast numbers of servers, centralized
queue-driven auto-scaling techniques involve a substantial communication
overhead and major implementation burden, or may not even be viable at all.
Motivated by the above issues, we propose a joint auto-scaling and load
balancing scheme which does not require any global queue length information or
explicit knowledge of system parameters, and yet provides provably near-optimal
service elasticity. We establish the fluid-level dynamics for the proposed
scheme in a regime where the total traffic volume and nominal service capacity
grow large in proportion. The fluid-limit results show that the proposed scheme
achieves asymptotic optimality in terms of user-perceived delay performance as
well as energy consumption. Specifically, we prove that both the waiting time
of tasks and the relative energy portion consumed by idle servers vanish in the
limit. At the same time, the proposed scheme operates in a distributed fashion
and involves only constant communication overhead per task, thus ensuring
scalability in massive data center operations.

本文提出一种联合自动扩展和负载均衡方案，不需要任何全局队列长度信息或系统参数的显式知识，并提供可证明近似最优的服务弹性，同时分布式地进行操作并仅涉及每个任务的常量通信开销，从而确保在大规模数据中心操作中具有可扩展性。

大规模分布式系统中的最优服务弹性

Optimal Service Elasticity in Large-Scale Distributed Systems

Scalability is an important characteristic of cloud computing. With
scalability, cost is minimized by provisioning and releasing resources
according to demand. Most of current Infrastructure as a Service (IaaS)
providers deliver threshold-based auto-scaling techniques. However, setting up
thresholds with right values that minimize cost and achieve Service Level
Agreement is not an easy task, especially with variant and sudden workload
changes. This paper has proposed dynamic threshold based auto-scaling
algorithms that predict required resources using Long Short-Term Memory
Recurrent Neural Network and auto-scale virtual resources based on predicted
values. The proposed algorithms have been evaluated and compared with some of
existing algorithms. Experimental results show that the proposed algorithms
outperform other algorithms.

该研究提出了一种基于机器学习和神经网络的动态阈值自动扩展算法，以实现云计算中资源的自适应扩展，实验结果表明该算法优于其他算法。