基于负载预测的微服务混合自动扩展

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, we used the microservice application in a real edge cloud environment to experimentally evaluate two synthesis and two actual workloads. And we proposed a hybrid autoscaling method based on workload prediction (Predictively Horizontal and Vertical Pod Auto-scaling, Pre-HVPA) , which first uses machine learning to carry out the workload data characteristics. After obtaining the final workload prediction result, we use 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.