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Containers as a lightweight technology to virtualise\ud applications have recently been successful, particularly to manage\ud applications in the cloud. Often, the management of clusters\ud of containers becomes essential and the orchestration of the\ud construction and deployment becomes a central problem. This\ud emerging topic has been taken up by researchers, but there is\ud currently no secondary study to consolidate this research. We\ud aim to identify, taxonomically classify and systematically compare\ud the existing research body on containers and their orchestration\ud and specifically the application of this technology in the cloud.\ud We have conducted a systematic mapping study of 46 selected\ud studies. We classified and compared the selected studies based\ud on a characterisation framework. This results in a discussion\ud of agreed and emerging concerns in the container orchestration\ud space, positioning it within the cloud context, but also moving it\ud closer to current concerns in cloud platforms, microservices and\ud continuous development

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Cloud elasticity provides a software system with the ability to maintain optimal user experience by automatically acquiring and releasing resources, while paying only for what has been consumed. The mechanism for automatically adding or removing resources on the fly is referred to as auto-scaling. The state-of-thepractice with respect to auto-scaling involves specifying thresholdbased rules to implement elasticity policies for cloud-based applications. However, there are several shortcomings regarding this approach. Firstly, the elasticity rules must be specified precisely by quantitative values, which requires deep knowledge and expertise. Furthermore, existing approaches do not explicitly deal with uncertainty in cloud-based software, where noise and unexpected events are common. This paper exploits fuzzy logic to enable qualitative specification of elasticity rules for cloud-based software. In addition, this paper discusses a control theoretical approach using type-2 fuzzy logic systems to reason about elasticity under uncertainties. We conduct several experiments to demonstrate that cloud-based software enhanced with such elasticity controller can robustly handle unexpected spikes in the workload and provide acceptable user experience. This translates into increased profit for the cloud application owner.