Alertissimo - a tool for orchestration of LSST broker streams

Teachers’ self-efficacy in managing classrooms is an important consideration when it comes to bringing educational robots to classrooms. Orchestration tools support teachers by providing awareness indicators of students’ progress as well as levers to control the flow of the lesson. We designed and evaluated the impact of an orchestration tool for a series of robot-based learning activities to teach a basic concept in mathematics to children, aged 7-10. Six teachers in primary schools across Switzerland used the orchestration tool to manage the activities in six sessions involving a total of ninety-one students. We observed teachers’ usage of the orchestration tool during the sessions and interviewed them after the sessions about the usefulness of these functionalities. Our findings show that even though teachers used the tool in different ways from each other, in general, it supported them in their classroom orchestration practices, mainly to manage the activity sequence and get aware of the robot technical failures and, to a lesser degree, get aware of students’ progress for the purpose of activity transitions and enriching class discussions. We discuss the theoretical implications of these results, relating our findings to the literature on classroom orchestration tool design, especially highlighting the importance of educational level and the type of learning technology as contextual factors affecting teachers’ usage of orchestration tools. We also provide implications for designing orchestration tools, focusing on the necessity of providing different types of awareness indicators and multiple options for activity management to fulfil the variety of teachers’ orchestration needs.

Teachers play an important role during student collaboration by monitoring and stimulating interactions between students that are effective for learning. In the present paper, we review existing research concerning orchestration tools developed for teachers that take data concerning collaborating students as input and provide analyses or visualizations of the data for the benefit of more effective teacher guidance of student collaboration. Studies were coded for their methodological design, the function of the orchestration tool (mirroring, alerting, or advising), the type of information that was provided to the teacher (cognitive or social), and on what level the influence of the tool was evaluated (teacher or student level). It was found that most studies had a descriptive or exploratory design, with small sample sizes. Most orchestration tools fulfilled a mirroring function. There was diversity in the type of information provided. Most included studies focused on the influence of the tool on the teacher, and those studies showed mixed findings on whether the orchestration tool enhanced their practice. Recommendations for future research are provided, and include the need for more systematic development and comparison of the various characteristics of orchestration tools.

Cloud Computing is an emerging technology that is used not only by developers but also by end-users. It has vital importance in the Information Technology (IT) industries as its future would create a great transition from conventional IT services. These days, containerization in cloud computing has become an important research area. The selection of container orchestration tools is one of the difficult tasks for the organizations involved in the management of the vast number of containers. These tools have their strengths, weaknesses, and functionalities which need to be considered. This paper presents a comparative analysis of the container orchestration tools. This analysis would help the professionals to decide whether they need an orchestrator bound to a single technology or an orchestrator which provides the independent solution. In this paper, four popular orchestration tools viz., Kubernetes, Docker Swarm, Mesos, and Redhat OpenShift are analyzed on various parameters viz., security, deployment, stability, scalability, cluster installation, and learning curve. We observed that Kubernetes has the best scheduling features whereas Docker Swarm is easy to use. We also found that Mesos has good scalability whereas OpenShift is a highly secure orchestration tool.

Accuracy and effectiveness of an orchestration tool on instructors’ interventions and groups’ collaboration

Compared to the traditional approach of using virtual machines as the basis for the development and deployment of applications running in Cloud-based infrastructures, container technology provides developers with a higher degree of portability and availability, allowing developers to build and deploy their applications in a much more efficient and flexible manner. A number of tools have been proposed to orchestrate complex applications comprising multiple containers requiring continuous monitoring and management actions to meet application-oriented and non-functional requirements. Different container orchestration tools provide different features that incur different overheads. As such, it is not always easy for developers to choose the orchestration tool that will best suit their needs. In this paper we compare the benefits and overheads incurred by the most popular open source container orchestration tools currently available, namely: Kubernetes and Docker in Swarm mode. We undertake a number of benchmarking exercises from well-known benchmarking tools to evaluate the performance overheads of container orchestration tools and identify their pros and cons more generally. The results show that the overall performance of Kubernetes is slightly worse than that of Docker in Swarm mode. However, Docker in Swarm mode is not as flexible or powerful as Kubernetes in more complex situations.

The Cloud Computing paradigm promoted the outsourcing of IT infrastructure and enterprise applications paving the way to save costs of building and maintaining computing infrastructures on-premise. In this environment, scale up of applications to attend demands in high peaks become easier and highly automated. Virtualization was a key technology to enable these characteristics. Nowadays, Container technology became popular as an alternative to Virtual Machines, and is being widely applied, as a consequence, Orchestration tools are being extensively applied in the Cloud environment. Despite its success, when it comes to the Internet of Things (IoT), Cloud Computing falls short to meet several requirements. Fog Computing appear as a complimentary technology to the Cloud to deliver the missing requirements in the IoT scene. Managing services deployed in a Fog Environment is a complex task and infrastructure management and orchestration tools can make it seamless. In this paper, we evaluate how Containers can affect the overall performance of applications in Fog Nodes. We analyze different Container Orchestration tools and how they meet Fog requirements to run applications. We also propose a Container Orchestration Framework for Fog Computing infrastructures.

The modern enterprise application ecosystem that containerization has become a part of has benefited containerization by greatly improving scalability, flexibility, and resource optimization. This research focuses on the key practices for adopting these technologies in enterprise operations. Docker is a tool that helps us develop and run software simply, better helping us package it fast and securely by creating containers of our apps with all the required dependencies inside them, assuring consistency across environments. Docker is Extended by an orchestration tool, Kubernetes, which automates the deployment, scaling, and collection of information in Kubernetes, which allows it to scale up or down for various reasons, such as increased or decreased computer resources, to handle the load balancing and provide fault tolerance. This paper also discusses the best practices for containerization in enterprise environments, such as security and performance optimization. In addition, combining Docker and Kubernetes within CI/CD pipelines eases seamless, automated, fast software delivery and reliability. The study also discusses the future trends of containerization, such as the developments in the orchestration tools, the insertion of AI and machine learning into containerized environments, and the surging of edge computing, as these things will help push the use of containerization in enterprise applications even more. The history of Docker and Kubernetes is seeing enterprises develop, deploy, and manage those apps in an increasingly agile, cost-effective, and scalable manner that suits the changing needs of business today.

Cloud-native application development is reshaping how modern organizations build, deploy, and manage software. This narrative review aims to synthesize recent literature on the adoption of cloud-native paradigms, particularly focusing on microservices architecture, containerization, orchestration tools, security frameworks, and AI-driven resource management. Using Scopus, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar as primary databases, the review applies Boolean keyword combinations to identify relevant peer-reviewed publications. Studies were selected based on their alignment with defined inclusion criteria, emphasizing empirical insights on cloud-native technologies. The findings reveal that microservices enhance system scalability and business agility, while containerization offers portability and efficient resource utilization. Orchestration tools, especially Kubernetes, enable automated deployment and management across complex environments. Security integration through DevSecOps and Policy-as-Code frameworks strengthens defense mechanisms against cyber threats. Furthermore, AI-supported orchestration improves efficiency in resource allocation and system responsiveness. The discussion underscores the necessity of systemic support, including organizational policies, talent development, and cross-functional collaboration, in ensuring successful adoption. This review concludes that cloud-native success demands more than technical innovation; it requires strategic alignment between technology, human capital, and governance. Policymakers and organizational leaders must invest in comprehensive frameworks that support security, adaptability, and continuous learning. Future studies should expand the scope by evaluating cloud-native transformations across industries and developing scalable best practices for AI integration and policy deployment.

Provenance of scientific data will play an increasingly critical role as scientists are encouraged by funding agencies and grand challenge problems to share and preserve scientific data. But it is foolhardy to believe that all human processes, particularly as varied as the scientific discovery process, will be fully automated by a workflow system. Consequently, provenance capture has to be thought of as a problem applied to both human and automated processes. The unmanaged workflow is the full human-driven activity, encompassing tasks whose execution is automated by an orchestration tool, and tasks that are done outside an orchestration tool. In this chapter we discuss the implications of the unmanaged workflow as it affects provenance capture, representation, and use. Illustrations of capture include multiple experiences with unmanaged capture using the Karma tool. Illustrations of use include defining workflows by suggesting additions to workflow designs under construction, reconstructing process traces, and using analysis tools to assess provenance quality.KeywordsData provenancee-Science workflowsprovenance capturedata miningcase-based reasoningintelligent user interfaces

Geo-distributed efficient deployment of containers with Kubernetes

Orchestrating collaborative learning activities is a challenge, even with the support of technology. Tangibles as orchestration tools represent an ambient and embodied approach to sharing information about the learning content and flow of the activity, thus facilitating both collaboration and its orchestration. Therefore, we propose tangibles as a solution to orchestrate productive collaborative learning. Concretely, this paper makes three contributions toward this end: First, we analyze the design space for tangibles as an orchestration tool to support collaborative learning and identify twelve essential dimensions. Second, we present five tangible tools for collaborative learning activities in face-to-face and online classrooms. Third, we present principles and challenges to designing tangibles for orchestrating collaborative learning based on our findings from the evaluation of ten educational experts who evaluated these tools using a usability scale and open questions. The key findings were: (1) they had good usability; (2) their main advantages are ease of use and support for collaborative learning; (3) their main disadvantages are limited functions and the difficulty to scale them to more users. We conclude by providing reflections and recommendations for the future design of tangibles for orchestration.

Internet of Things (IoT) is the developing technology that enables devices to communicate without human interaction. IoT utilizes cloud computing services to collect and process data for IoT devices and to manage the device remotely. Cloud computing is not efficient enough to handle the fast stream of data produced by the IoT, therefore scaling up IoT applications to meet demands of high peak becomes easier and highly automated in fog computing. Containers are mostly used as virtualization solutions for IoT in fog computing. It enables the execution of small microservices to large applications. However, the rise of many lightweight containers has resulted in new application architectures and fundamentally changing how applications are deployed and visualized. Due to this change, container orchestration tools were proposed. These tools allow users to coordinate and manage containers. However, container orchestration tools need to meet the requirements of IoT applications and constraints imposed on the nodes in fog. This paper presents a systematic literature review on the selection of orchestration tools for the efficient deployment of IoT applications in fog computing. Moreover, the performance of IoT applications must be considered by applying different metrics. This paper aims to propose potential research directions to address identified gaps in the selection of orchestration tools.

The last five years can safely be termed a Kubernetes Tsunami in the IT world. Kubernetes has been around since 2014, and it conquered not only the service catalogs of the major cloud providers but also most data centers around the world. Looking at the statistics reveals that if you want to run workloads in containers at scale, there is actually no other container orchestration tool around. In a report from Red Hat from 2021 asking organizations which container orchestration tool they use, you would still see mention of things like Mesosphere and Docker Swarm, but without a notable share and only with news coverage talking about “end-of-life.”

Modern developers typically run their workloads through cloud-native environments such as Docker and Kubernetes. Docker is a platform that runs and manages containers. With the birth of Docker, interest in containers and technology has grown. As one of the container orchestration tools that control and manage containers running on multiple hosts, Kubernetes has a very large share and is used by many cloud companies, making it the standard for practical container orchestration tools. Therefore, in this paper, by analyzing the Kubernetes event timeline, we present the future direction of Kubernetes and Docker, which are key tools in the cloud-native environment.

Containerization is a cutting-edge DevOps technology which unifies the IT operations and Development domains. In recent times, virtualization using Virtual Machines has become an overkill for its large overhead on systems. As a lightweight alternative, containerization offers containers that constitute a package of an application along with all its dependencies that is required for it to execute. Containerization platforms help in building containers from images. Docker is a widely popular containerization platform. Containerization Orchestration tools manage these containers. Kubernetes is the front-runner of the emerging market of container orchestration tools. These software work together seamlessly in order to successfully implement containerization both locally and on the cloud. In this paper, we aim to deploy the container orchestration tool Kubernetes on a local system with a Docker sample container. The purpose of this is to ensure that all the configurations and management needed for a Docker container is set successfully on the local system before it is deployed onto the cloud or on the premise. The on-premise deployment use case is very important in domains such as finance and healthcare where organizations hesitate to upload confidential information on to the cloud for security reasons but still require scaling of their applications.