The Experts below are selected from a list of 1917 Experts worldwide ranked by ideXlab platform
Udo R. Krieger - One of the best experts on this subject based on the ideXlab platform.
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on a fog computing platform built on arm architectures by Docker Container technology
International Conference on Supercomputing, 2017Co-Authors: Andreas Eiermann, Mathias Renner, Marcel Grosmann, Udo R. KriegerAbstract:Fog computing constitutes currently a challenging effort to establish the concepts and services of cloud computing at the edge of converging wireless networks and wired high-speed backbones. We discuss the concepts of our fog computing platform HCL-BaFog. It is built on top of Hypriot Cluster Lab (HCL) which has been developed by the Hypriot Pirate Crew in recent years based on single board computers with an ARM architecture. It uses LINUX Container technology as underlying open source platform that has been established by means of the rapidly evolving framework Docker. We present the design principles of our fog computing platform and discuss its different software components. To create clusters of fog cells subject to high-availability requirements and to provide failsafe data processing, we further summarize some performance results on the integration of the orchestration tools Docker Swarm Mode and Kubernetes on HCL and draw some conclusions regarding their suitability for fog computing.
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On a fog computing platform built on ARM architectures by Docker Container technology
Communications in Computer and Information Science, 2017Co-Authors: Andreas Eiermann, Marcel Großmann, Martin Grossmann, Mathias Renner, Udo R. KriegerAbstract:© Springer International Publishing AG 2017. Fog computing constitutes currently a challenging effort to establish the concepts and services of cloud computing at the edge of converging wireless networks and wired high-speed backbones. We discuss the concepts of our fog computing platform HCL-BaFog. It is built on top of Hypriot Cluster Lab (HCL) which has been developed by the Hypriot Pirate Crew in recent years based on single board computers with an ARM architecture. It uses LINUX Container technology as underlying open source platform that has been established by means of the rapidly evolving framework Docker. We present the design principles of our fog computing platform and discuss its different software components. To create clusters of fog cells subject to high-availability requirements and to provide failsafe data processing, we further summarize some performance results on the integration of the orchestration tools Docker Swarm Mode and Kubernetes on HCL and draw some conclusions regarding their suitability for fog computing.
David De Roure - One of the best experts on this subject based on the ideXlab platform.
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a framework for the preservation of a Docker Container
International Journal of Digital Curation, 2018Co-Authors: Iain Emsley, David De RoureAbstract:Reliably building and maintaining systems across environments is a continuing problem. A project or experiment may run for years. Software and hardware may change as can the operating system. Containerisation is a technology that is used in a variety of companies, such as Google, Amazon and IBM, and scientific projects to rapidly deploy a set of services repeatably. Using Dockerfiles to ensure that a Container is built repeatably, to allow conformance and easy updating when changes take place are becoming common within projects. Its seen as part of sustainable software development. Containerisation technology occupies a dual space: it is both a repository of software and software itself. In considering Docker in this fashion, we should verify that the Dockerfile can be reproduced. Using a subset of the Dockerfile specification, a domain specific language is created to ensure that Docker files can be reused at a later stage to recreate the original environment. We provide a simple framework to address the question of the preservation of Containers and its environment. We present experiments on an existing Dockerfile and conclude with a discussion of future work. Taking our work, a pipeline was implemented to check that a defined Dockerfile conforms to our desired model, extracts the Docker and operating system details. This will help the reproducibility of results by creating the machine environment and package versions. It also helps development and testing through ensuring that the system is repeatably built and that any changes in the software environment can be equally shared in the Dockerfile. This work supports not only the citation process it also the open scientific one by providing environmental details of the work. As a part of the pipeline to create the Container, we capture the processes used and put them into the W3C PROV ontology. This provides the potential for providing it with a persistent identifier and traceability of the processes used to preserve the metadata. Our future work will look at the question of linking this output to a workflow ontology to preserve the complete workflow with the commands and parameters to be given to the Containers. We see this provenance within the build process useful to provide a complete overview of the workflow.
Andreas Eiermann - One of the best experts on this subject based on the ideXlab platform.
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on a fog computing platform built on arm architectures by Docker Container technology
International Conference on Supercomputing, 2017Co-Authors: Andreas Eiermann, Mathias Renner, Marcel Grosmann, Udo R. KriegerAbstract:Fog computing constitutes currently a challenging effort to establish the concepts and services of cloud computing at the edge of converging wireless networks and wired high-speed backbones. We discuss the concepts of our fog computing platform HCL-BaFog. It is built on top of Hypriot Cluster Lab (HCL) which has been developed by the Hypriot Pirate Crew in recent years based on single board computers with an ARM architecture. It uses LINUX Container technology as underlying open source platform that has been established by means of the rapidly evolving framework Docker. We present the design principles of our fog computing platform and discuss its different software components. To create clusters of fog cells subject to high-availability requirements and to provide failsafe data processing, we further summarize some performance results on the integration of the orchestration tools Docker Swarm Mode and Kubernetes on HCL and draw some conclusions regarding their suitability for fog computing.
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On a fog computing platform built on ARM architectures by Docker Container technology
Communications in Computer and Information Science, 2017Co-Authors: Andreas Eiermann, Marcel Großmann, Martin Grossmann, Mathias Renner, Udo R. KriegerAbstract:© Springer International Publishing AG 2017. Fog computing constitutes currently a challenging effort to establish the concepts and services of cloud computing at the edge of converging wireless networks and wired high-speed backbones. We discuss the concepts of our fog computing platform HCL-BaFog. It is built on top of Hypriot Cluster Lab (HCL) which has been developed by the Hypriot Pirate Crew in recent years based on single board computers with an ARM architecture. It uses LINUX Container technology as underlying open source platform that has been established by means of the rapidly evolving framework Docker. We present the design principles of our fog computing platform and discuss its different software components. To create clusters of fog cells subject to high-availability requirements and to provide failsafe data processing, we further summarize some performance results on the integration of the orchestration tools Docker Swarm Mode and Kubernetes on HCL and draw some conclusions regarding their suitability for fog computing.
Baek Yong Choi - One of the best experts on this subject based on the ideXlab platform.
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Application deployment using Microservice and Docker Containers: Framework and optimization
Journal of Network and Computer Applications, 2018Co-Authors: Xili Wan, Guangwei Bai, Tianjing Wang, Xinjie Guan, Baek Yong ChoiAbstract:To improve the scalability and elasticity of application deployment and operation in cloud computing environments, new architectures and techniques are developed and studied, e.g., microservice architecture, and Docker Container. Especially, Docker Container enables the sharing on operation system and supporting libraries, which is more lightweight, prompt and scalable than Hypervisor based virtualization. These features make it ideally suit for applications deployed in microservice architecture. However, existing models and schemes, which are mostly designed for Hypervisor based virtualization techniques, fall short to be efficiently used for Docker Container based application deployment. To take the benefits of microservice architecture and Docker Containers, we explore the optimization of application deployment in cloud data centers using microservice and Docker Containers. Our goal is to minimize the application deployment cost as well as the operation cost while preserving service delay requirements for applications. In this paper, we first formulate the application deployment problem by examining the features of Docker, the requirements of microservice-based applications, and available resources in cloud data centers. We further propose a communication efficient framework and a suboptimal algorithm to determine the Container placement and task assignment. The proposed algorithm works in a distributed and incremental manner, which makes it scalable to massive physical resources and diverse applications under the framework. We validate the efficiency of our solution through comparisons with three existing strategies in Docker Swarm using real traces from Google Cluster. The evaluation results show that the proposed framework and algorithm provide more flexibility and save more cost than existing strategies.
Iain Emsley - One of the best experts on this subject based on the ideXlab platform.
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a framework for the preservation of a Docker Container
International Journal of Digital Curation, 2018Co-Authors: Iain Emsley, David De RoureAbstract:Reliably building and maintaining systems across environments is a continuing problem. A project or experiment may run for years. Software and hardware may change as can the operating system. Containerisation is a technology that is used in a variety of companies, such as Google, Amazon and IBM, and scientific projects to rapidly deploy a set of services repeatably. Using Dockerfiles to ensure that a Container is built repeatably, to allow conformance and easy updating when changes take place are becoming common within projects. Its seen as part of sustainable software development. Containerisation technology occupies a dual space: it is both a repository of software and software itself. In considering Docker in this fashion, we should verify that the Dockerfile can be reproduced. Using a subset of the Dockerfile specification, a domain specific language is created to ensure that Docker files can be reused at a later stage to recreate the original environment. We provide a simple framework to address the question of the preservation of Containers and its environment. We present experiments on an existing Dockerfile and conclude with a discussion of future work. Taking our work, a pipeline was implemented to check that a defined Dockerfile conforms to our desired model, extracts the Docker and operating system details. This will help the reproducibility of results by creating the machine environment and package versions. It also helps development and testing through ensuring that the system is repeatably built and that any changes in the software environment can be equally shared in the Dockerfile. This work supports not only the citation process it also the open scientific one by providing environmental details of the work. As a part of the pipeline to create the Container, we capture the processes used and put them into the W3C PROV ontology. This provides the potential for providing it with a persistent identifier and traceability of the processes used to preserve the metadata. Our future work will look at the question of linking this output to a workflow ontology to preserve the complete workflow with the commands and parameters to be given to the Containers. We see this provenance within the build process useful to provide a complete overview of the workflow.