LAUSR

Container orchestration platforms such as Kubernetes and Kubernetes-derived KubeEdge (called Kubernetes-based systems collectively) have been gradually used to conduct unified management of Cloud, Fog, and Edge resources. Container provisioning algorithms are crucial to guaranteeing quality of services (QoS) of such Kubernetes-based systems. However, most existing algorithms focus on placement and migration of fixed number of containers without considering elastic provisioning of containers. Meanwhile, widely used linear-performance-model-based feedback control or fixed-processing-rate-based queuing model on diverse platforms cannot describe the performance of containerized Web systems accurately. Furthermore, a fixed reference point used by existing methods is likely to generate inaccurate output errors incurring great fluctuations encountered with large arrival-rate changes. In this article, a feedback control method is designed based on a combination of varying-processing-rate queuing model and linear-model to provision containers elastically which improves the accuracy of output errors by learning reference models for different arrival rates automatically and mapping output errors from reference models to the queuing model. Our approach is compared with several state-of-art algorithms on a real Kubernetes cluster. Experimental results illustrate that our approach obtains the lowest percentage of service level agreement (SLA) violation (decreasing no less than 8.44 percent) and the second lowest cost.