Elasticity is a fundamental property in cloud computing that has recently witnessed major developments. This article reviews both classical and recent elasticity solutions and provides an overview of containerization, a new technological trend in lightweight virtualization. It also discusses major issues and research challenges related to elasticity in cloud computing. We comprehensively review and analyze the proposals developed in this field. We provide a taxonomy of elasticity mechanisms according to the identified works and key properties. Compared to other works in literature, this article presents a broader and detailed analysis of elasticity approaches and is considered as the first survey addressing the elasticity of containers.

Elasticity is the key feature of cloud computing to scale computing resources according to application workloads timely. In the literature as well as in industrial products, much attention was given to the elasticity of virtual machines, but much less to the elasticity of containers. However, containers are the new trend for packaging and deploying microservices-based applications. Moreover, most of approaches focus on horizontal elasticity, fewer works address vertical elasticity. In this paper, we propose ELASTICDOCKER, the first system powering vertical elasticity of Docker containers autonomously. Based on the well-known IBM's autonomic computing MAPE-K principles, ELASTICDOCKER scales up and down both CPU and memory assigned to each container according to the application workload. As vertical elasticity is limited to the host machine capacity, ELASTICDOCKER does container live migration when there is no enough resources on the hosting machine. Our experiments show that ELASTICDOCKER helps to reduce expenses for container customers, make better resource utilization for container providers, and improve Quality of Experience for application end-users. In addition, based on the observed migration performance metrics, the experiments reveal a high efficient live migration technique. As compared to horizontal elasticity, ELASTICDOCKER outperforms Kubernetes elasticity by 37.63%.

Cloud platforms are increasingly being used for hosting a broad diversity of services from traditional e-commerce applications to interactive web-based Ides. How-ever, we observe that the proliferation of offers by cloud providers raises several challenges. Developers will not only have to deploy applications for a specific cloud, but will also have to consider migrating services from one cloud to another, and to manage distributed applications spanning multiple clouds. In this paper, we present our federated multi-cloud PaaS infrastructure for addressing these challenges. This infrastructure is based on three foundations: i) an open service model used to design and implement both our multi-cloud PaaSand the SaaS applications running on top of it, ii) a configurable architecture of the federated PaaS, and iii) some infrastructure services for managing both our multi-cloud PaaS and the SaaS applications. We then show how this multi-cloud PaaS can be deployed on top of thirteen existing IaaS/PaaS. We finally report on three distributed SaaS applications developed with and deployed on our federated multi-cloud PaaS infrastructure.

The Service Component Architecture (SCA) is a technology agnostic standard for developing and deploying distributed service-oriented applications. However, SCA does not define standard means for runtime manageability (including introspection and reconfiguration) of SOA applications and of their supporting environment. This paper presents the FraSCAti platform, which brings runtime management features to SCA, and discusses key principles in its design: the adoption of an extended SCA component model for the implementation of SOA applications and of the FraSCAti platform itself; the use of component-based interception techniques for dynamically weaving non-functional services such as transaction management with components. The paper presents micro-benchmarks that show that runtime manageability in the FraSCAti platform is achieved without hindering its performance relative to the de facto reference SCA implementation, Apache's Tuscany.

With the emergence of Docker, it becomes easier to encapsulate applications and their dependencies into lightweight Linux containers and make them available to the world by deploying them in the cloud. Compared to hypervisor-based virtualization approaches, the use of containers provides faster start-ups times and reduces the consumption of computer resources. However, Docker lacks of deployability verification tool for containers at design time. Currently, the only way to be sure that the designed containers will execute well is to test them in a running system. If errors occur, a correction is made but this operation can be repeated several times before the deployment becomes operational. Docker does not provide a solution to increase or decrease the size of container resources in demand. Besides the deployment of containers, Docker lacks of synchronization between the designed containers and those deployed. Moreover, container management with Docker is done at low level, and therefore requires users to focus on low level system issues. In this paper we focus on these issues related to the management of Docker containers. In particular, we propose an approach for modeling Docker containers. We provide tooling to ensure the deployability and the management of Docker containers. We illustrate our proposal using an event processing application and show how our solution provides a significantly better compromise between performance and development costs than the basic Docker container solution.