Multitenant Containers as a Service (CaaS) for Clouds and Edge Clouds

Cloud computing, offering on-demand access to computing resources through the Internet and the pay-as-you-go model, has marked the last decade with its three main service models; Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The lightweight nature of containers compared to virtual machines has led to the rapid uptake of another in recent years, called Containers as a Service (CaaS), which falls between IaaS and PaaS regarding control abstraction. However, when CaaS is offered to multiple independent users, or tenants, a multi-instance approach is used, in which each tenant receives its own separate cluster, which reimposes significant overhead due to employing virtual machines for isolation. If CaaS is to be offered not just at the cloud, but also at the edge cloud, where resources are limited, another solution is required. We introduce a native CaaS multitenancy framework, meaning that tenants share a cluster, which is more efficient than the one tenant per cluster model. Whenever there are shared resources, isolation of multitenant workloads is an issue. Such workloads can be isolated by Kata Containers today. Besides, our framework esteems the application requirements that compel complete isolation and a fully customized environment. Node-level slicing empowers tenants to programmatically reserve isolated subclusters where they can choose the container runtime that suits application needs. The framework is publicly available as liberally-licensed, free, open-source software that extends Kubernetes, the de facto standard container orchestration system. It is in production use within the EdgeNet testbed for researchers

Mobile devices use in analyzing the engineering students attitude towards programming by using a fuzzy logic technique

The aim of this study is to use mobile devices in the determination of engineering students' attitudes towards programming by using a fuzzy logic technique. First of all, a mobile game that is played by engineering students is developed to make learning programming more enjoyable. After that, the proposed fuzzy logic-based attitude determination system which runs on mobile devices comes into play. Student answers and gives points between 1 and 5 to the survey questions which are presented by the developed mobile application. These points are first evaluated in the fuzzification step by using membership functions and then the fuzzied input is given to the rule base step. To get crisp output value, fuzzied output is defuzzified at the last step of the fuzzy logic-based system. Hence the attitude of the student towards programming is inferenced. The developed system is carried out with 100 first-grade students of the software engineering department. Frequency, mean, standard deviation, normality, t test, and analysis of variance (ANOVA) analyses are performed with the obtained data. Results show that the proposed fuzzy logic-based system performs much better than the classical approach. As a result of Article Reliability Analysis of the Attitude Scale Towards Mobile Learning, the scale is found highly reliable. A significant difference is found in favor of fuzzy logic-based attitude score among classical logic-based attitude scores as a result of the paired-samples t test. The results of t test and ANOVA tests according to gender, mother, and father education levels are found not statistically significant

Multitenant Containers as a Service (CaaS) for Clouds and Edge Clouds

In recent years, along with containers, the cloud community has rapidly taken up Kubernetes, the de facto industry standard container orchestration system. All major cloud providers currently offer Kubernetes-based Containers as a Service (CaaS). However, when CaaS is offered to multiple independent consumers, or tenants, a multi-instance approach is used, in which each tenant receives its own separate cluster, which imposes significant overhead due to employing virtual machines for isolation. If CaaS is to be offered not only in the cloud, but also in the edge cloud, where resources are limited, another solution is required. In this paper, drawing upon the scientific literature, we provide a novel classification of Kubernetes multitenancy into three approaches: multi-instance through multiple clusters, multi-instance through multiple control planes, and single-instance native. We propose a single-instance multitenancy framework, meaning tenants are served out of a shared control plane in a single cluster. Our empirical findings show that the single-instance approach imposes a markedly decreased overhead compared to the other two. However, it entails a tradeoff in workload isolation owing to tenants sharing the compute nodes. There are nonetheless means to compensate for such weakened isolation, and we describe how our framework does so. The framework is publicly available as liberally-licensed, free, open-source software that extends Kubernetes. It is in production use within the EdgeNet testbed for researchers

Federating EdgeNet with Fed4FIRE+ and Deploying its Nodes Behind NATs

International audienceEdgeNet is a globally-distributed edge cloud testbed. It aims to provide an experimentation platform to computer networks and distributed systems researchers. However, distributed testbed providers encounter difficulties with node provisioning, access, and maintenance when establishing an edge cloud consisting of ubiquitous nodes. EdgeNet extends Kubernetes to the edge and addresses these challenges. Employing an industrystandard container orchestration system enables researchers to straightforwardly deploy experiments across many vantage points, maximizing their time for collecting and analyzing measurement data. This paper describes three features that we have developed as liberally-licensed, free, open-source extensions to Kubernetes: federation, nodes in home networks, and easy node deployment. We also discuss remote node maintenance as future work