A Performance Evaluation of Containers Running on Managed Kubernetes Services Containerbased virtualisation technologies are gaining more and more traction in recent years across Cloud platforms and this will likely continue in the coming years. As such, containers orchestration technologies are becoming indispensable. Kubernetes has become the de facto standard because of its robustness, maturity and rich features. To free users of the burden of having to configure and maintain complex Kubernetes infrastructures, but still make use of its functionalities, all major Cloud providers are now offering cloudnative managed Kubernetes alternatives. The goal of this paper is to investigate the performance of containers running in such hosted services. For this purpose, we conduct a series of experimental evaluations of containers to monitor the behaviour of system resources including CPU, memory, disk and network. A baseline consisting of a manually deployed Kubernetes cluster was built for comparison. In particular, we consider the Amazon Elastic Container Service for Kubernetes (EKS), Microsoft Azure Kubernetes Service (AKS) and Google Kubernetes Engine (GKE). The Australiawide NeCTAR Research Cloud was used for the baseline.

Evaluating Performance of Active Containers on PaaS Fog under Batch Arrivals: A Modeling Approach Modelbased performance evaluation of largescale PaaS Fog Datacenter requires to develop a hierarchical model, which is usually composed of a series of monolithic models. This paper proposes an approximate analytic modeling approach to evaluate the performance of a pool of active containers on a PaaS Fog physical node under batch task arrivals, by using an MmathrmxGmmK queue. We describe the details of the proposed model and the formulas for calculating performance measures of interest. Experiment results indicate that the proposed approach (including the model and the formulas) can approximately capture the system behaviors even when the task servicetime distribution has a large coefficient of variation (u003e1.5).