Adaptive processing rate based container provisioning for meshed Micro-services in Kubernetes Clouds

More and more applications are organized in the form of meshed micro-services which can be deployed on the popular container orchestration platform Kubernetes. Designing appropriate container auto-scaling methods for such applications in Kubernetes is beneficial to reducing costs and guaranteeing Quality of Services (QoS). However, most existing resource provisioning methods focus on a service without considering interactions among meshed services. Meanwhile, synchronous calls among services have different impacts on the processing ability of containers as the proportion of different business type's requests changes which is not considered in existing methods too. Therefore, in this article, an adaptive queuing model and queuing-length aware Jackson queuing network based method is proposed. It adjusts the processing rate of containers according to the ratio of synchronous calls and considers queuing tasks when calculating the impact of bottleneck tiers to others. Experiments are performed on a real Kubernetes cluster, which illustrate that the proposal obtains the lowest percentage of Service Level Agreement (SLA)-violations (decreasing about 6.33%-12.29%) with about 0.9% additional costs compared with existing methods of Kubernetes and other latest methods.