基于负载预测的微服务混合自动扩展

由于边缘云没有比中心云更强大的计算处理能力，在应对动态负载时很容易导致无意义的扩展抖动或资源处理能力不足的问题，所以在一个真实的边缘云环境中对微服务应用程序使用两个合成和两个实际工作负载进行实验评估，并提出了一种基于负载预测的混合自动扩展方法（predictively horizontal and vertical pod autoscaling，Pre-HVPA）。该方法首先采用机器学习对负载数据特征进行预测，并获得最终负载预测结果。然后利用预测负载进行水平和垂直的混合自动扩展。仿真结果表明，基于该方法所进行自动扩展可以减少扩展抖动和容器使用数量，所以适用于边缘云环境中的微服务应用。

Hybrid autoscaling of microservices based on workload prediction

Since edge clouds are not more powerful than the central cloud, it is easy to lead to unexpected autoscaling or low resource processing capabilities in response to dynamic workload. Therefore, this paper used the microservice application in a real edge cloud environment to experimentally evaluate two synthesis and two actual workloads. And this paper proposed a hybrid autoscaling method based on workload prediction, which first used machine learning to carry out the workload data characteristics. After obtaining the final workload prediction result, this paper used the predictive workload for hybrid autoscaling module. The simulation shows that the microservice autoscaling policy based on this method can reduce more scaled jitter and more pod container use, and the method is scalable, so it is suitable for the microservice applications in edge cloud environment.