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Journal articles on the topic 'Docker container orchestration'

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1

Kim, Dongmin, Hanif Muhammad, Eunsam Kim, Sumi Helal, and Choonhwa Lee. "TOSCA-Based and Federation-Aware Cloud Orchestration for Kubernetes Container Platform." Applied Sciences 9, no. 1 (2019): 191. http://dx.doi.org/10.3390/app9010191.

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Kubernetes, a container orchestration tool for automatically installing and managing Docker containers, has recently begun to support a federation function of multiple Docker container clusters. This technology, called Kubernetes Federation, allows developers to increase the responsiveness and reliability of their applications by distributing and federating container clusters to multiple service areas of cloud service providers. However, it is still a daunting task to manually manage federated container clusters across all the service areas or to maintain the entire topology of cloud applicati
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Farshteindiker, Adi, and Rami Puzis. "Leadership Hijacking in Docker Swarm and Its Consequences." Entropy 23, no. 7 (2021): 914. http://dx.doi.org/10.3390/e23070914.

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With the advent of microservice-based software architectures, an increasing number of modern cloud environments and enterprises use operating system level virtualization, which is often referred to as container infrastructures. Docker Swarm is one of the most popular container orchestration infrastructures, providing high availability and fault tolerance. Occasionally, discovered container escape vulnerabilities allow adversaries to execute code on the host operating system and operate within the cloud infrastructure. We show that Docker Swarm is currently not secured against misbehaving manag
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Truyen, Eddy, Dimitri Van Landuyt, Davy Preuveneers, Bert Lagaisse, and Wouter Joosen. "A Comprehensive Feature Comparison Study of Open-Source Container Orchestration Frameworks." Applied Sciences 9, no. 5 (2019): 931. http://dx.doi.org/10.3390/app9050931.

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(1) Background: Container orchestration frameworks provide support for management ofcomplex distributed applications. Different frameworks have emerged only recently, and they havebeen in constant evolution as new features are being introduced. This reality makes it difficult forpractitioners and researchers to maintain a clear view of the technology space. (2) Methods: wepresent a descriptive feature comparison study of the three most prominent orchestrationframeworks: Docker Swarm, Kubernetes, and Mesos, which can be combined with Marathon,Aurora or DC/OS. This study aims at (i) identifying
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Castro León, José. "Advanced features of the CERN OpenStack Cloud." EPJ Web of Conferences 214 (2019): 07026. http://dx.doi.org/10.1051/epjconf/201921407026.

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The CERN OpenStack cloud has been delivering a wide variety of services to the whole laboratory since it entered in production in 2013. Initially, standard resources such as Virtual Machines and Block Storage were offered. Today, the cloud offering includes advanced features such as Container Orchestration (for Kubernetes, Docker Swarm mode, Mesos/DCOS clusters), File Shares and Bare Metal, and the Cloud team is preparing the addition of Networking and Workflow-as-a-Service components. In this paper, we will describe these advanced features, the OpenStack projects that provide them, as well as
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Bocchi, Enrico, Luca Canali, Diogo Castro, et al. "ScienceBox Converging to Kubernetes containers in production for on-premise and hybrid clouds for CERNBox, SWAN, and EOS." EPJ Web of Conferences 245 (2020): 07047. http://dx.doi.org/10.1051/epjconf/202024507047.

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Docker containers are the de-facto standard to package, distribute, and run applications on cloud-based infrastructures. Commercial providers and private clouds expand their offer with container orchestration engines, making the management of resources and containerized applications tightly integrated. The Storage Group of CERN IT leverages on container technologies to provide ScienceBox: An integrated software bundle with storage and computing services for general purposes and scientific use. ScienceBox features distributed scalable storage, sync&share functionalities, and a web-based dat
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Bhat, Pranava. "Centralised Cleanup Service for Kubernetes and Cloud Resources." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1545–49. http://dx.doi.org/10.22214/ijraset.2021.36648.

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The architectural style of developing a software application using loosely coupled and highly cohesive services can be termed as microservices architecture. The microservices allow agile software development and enable businesses to build and deliver applications quickly. To achieve the benefits of microservices, an underlying infrastructure that supports them must exist. This includes CI/CD pipelines, execution environments like virtual machines and containers, logging and monitoring, communication mechanisms, and so on. Containers are lightweight, enable multiple execution environments to ex
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Taherizadeh, Salman, Vlado Stankovski, and Marko Grobelnik. "A Capillary Computing Architecture for Dynamic Internet of Things: Orchestration of Microservices from Edge Devices to Fog and Cloud Providers." Sensors 18, no. 9 (2018): 2938. http://dx.doi.org/10.3390/s18092938.

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The adoption of advanced Internet of Things (IoT) technologies has impressively improved in recent years by placing such services at the extreme Edge of the network. There are, however, specific Quality of Service (QoS) trade-offs that must be considered, particularly in situations when workloads vary over time or when IoT devices are dynamically changing their geographic position. This article proposes an innovative capillary computing architecture, which benefits from mainstream Fog and Cloud computing approaches and relies on a set of new services, including an Edge/Fog/Cloud Monitoring Sys
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Raza, Syed M., Jaeyeop Jeong, Moonseong Kim, Byungseok Kang, and Hyunseung Choo. "Empirical Performance and Energy Consumption Evaluation of Container Solutions on Resource Constrained IoT Gateways." Sensors 21, no. 4 (2021): 1378. http://dx.doi.org/10.3390/s21041378.

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Containers virtually package a piece of software and share the host Operating System (OS) upon deployment. This makes them notably light weight and suitable for dynamic service deployment at the network edge and Internet of Things (IoT) devices for reduced latency and energy consumption. Data collection, computation, and now intelligence is included in variety of IoT devices which have very tight latency and energy consumption conditions. Recent studies satisfy latency condition through containerized services deployment on IoT devices and gateways. They fail to account for the limited energy a
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Tapia, Freddy, Miguel Ángel Mora, Walter Fuertes, Jorge Edison Lascano, and Theofilos Toulkeridis. "A Container Orchestration Development that Optimizes the Etherpad Collaborative Editing Tool through a Novel Management System." Electronics 9, no. 5 (2020): 828. http://dx.doi.org/10.3390/electronics9050828.

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The use of collaborative tools has notably increased recently. It is common to see distinct users that need to work simultaneously on shared documents. In most cases, large companies provide tools whose implementations have been a very complicated and expensive task. Likewise, their platform deployment requirements should be robust hardware infrastructures. It becomes even more critical when their main target is to reach scalability and highavailability. Therefore, this study aims to design and implement a microservices-based collaborative architecture using assembled containers in the cloud,
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Sharma, Mayank, Eraldo Silva Junior, Boris Iliev Vasilev, Maarten Litmaath, and Renato Santana. "The SIMPLE Framework for deploying containerized grid services." EPJ Web of Conferences 245 (2020): 07017. http://dx.doi.org/10.1051/epjconf/202024507017.

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The Worldwide LHC Computing Grid (WLCG) currently has about 170 sites. In order to support WLCG workloads, each site has to deploy and maintain a number of possibly complex grid services. Quite often, site managers require assistance of WLCG experts, for example when new software versions need to be deployed. Modern configuration management (e.g. Puppet, Ansible), container orchestration (e.g. Docker Swarm, Kubernetes) and containerization technologies (e.g. Docker, Podman) can help make such activities more lightweight by means of packaging sensible configurations of grid services and providi
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Sharma, Mayank, Maarten Litmaath, Eraldo Silva Junior, and Renato Santana. "Lightweight WLCG Sites." EPJ Web of Conferences 214 (2019): 07019. http://dx.doi.org/10.1051/epjconf/201921407019.

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This article describes a new framework, called SIMPLE, for settingup and maintaining classic WLCG sites with minimal operational efforts and insights needed into the WLCG middleware. The framework provides a single common interface to install and configure any of its supported grid services, such as Compute Elements, Batch Systems, Worker Nodes and miscellaneous middleware packages. It leverages modern container orchestration tools like Kubernetes, Docker Swarm, and confiuration management tools like Puppet, Ansible, to automate deployment of the WLCG services on behalf of a site admin. The fr
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Popov, S. E., R. Yu Zamaraev, N. I. Yukina, et al. "Software for Calculating Deformations of the Earth's Surface using Satellite Radar Data." Programmnaya Ingeneria 12, no. 5 (2021): 246–59. http://dx.doi.org/10.17587/prin.12.246-259.

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The article presents a description of a software package for calculating displacement rates and detecting displacements of the earths surface over areas of intensive coal mining. The complex is built on the basis of the microservice architecture Docker Swarm in integration with the system of massively parallel execution of tasks Apache Spark, as a high-level tool for organizing container-type computations with orchestration of hardware resources. In the software package, the container is used as an element of the sequence of calculation stages of the mathematical model of interferometric proce
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Fayos-Jordan, Rafael, Santiago Felici-Castell, Jaume Segura-Garcia, Adolfo Pastor-Aparicio, and Jesus Lopez-Ballester. "Elastic Computing in the Fog on Internet of Things to Improve the Performance of Low Cost Nodes." Electronics 8, no. 12 (2019): 1489. http://dx.doi.org/10.3390/electronics8121489.

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The Internet of Things (IoT) is a network widely used with the purpose of connecting almost everything, everywhere to the Internet. To cope with this goal, low cost nodes are being used; otherwise, it would be very expensive to expand so fast. These networks are set up with small distributed devices (nodes) that have a power supply, processing unit, memory, sensors, and wireless communications. In the market, we can find different alternatives for these devices, such as small board computers (SBCs), e.g., Raspberry Pi (RPi)), with different features. Usually these devices run a coarse version
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14

Großmann, Marcel, Steffen Illig, and Cornelius L. Matějka. "SensIoT: An Extensible and General Internet of Things Monitoring Framework." Wireless Communications and Mobile Computing 2019 (March 7, 2019): 1–15. http://dx.doi.org/10.1155/2019/4260359.

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SensIoT is an open-source sensor monitoring framework for the Internet of Things, which utilizes proven technologies to enable easy deployment and maintenance while staying flexible and scalable. It closes the gap between highly specialized and, therefore, inflexible sensor monitoring solutions, which are only adjusted to a specific context, and the development of every other solution from scratch. Our framework fits a variety of use cases by providing an easy to set up, extensible, and affordable solution. The development is based on our former published framework MonTreAL, whose goal is to o
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15

Dziurzanski, Piotr, Shuai Zhao, Michal Przewozniczek, Marcin Komarnicki, and Leandro Soares Indrusiak. "Scalable distributed evolutionary algorithm orchestration using Docker containers." Journal of Computational Science 40 (February 2020): 101069. http://dx.doi.org/10.1016/j.jocs.2019.101069.

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16

Nayak, Deekshith, and Dr H. V. Ravish Aradhya. "Orchestrating a stateful application using Operator." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 514–20. http://dx.doi.org/10.51201/jusst/21/05278.

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Containerization is a leading technological advancement in cloud-native developments. Virtualization isolates the running processes at the bare metal level but containerization isolates the processes at the operating system level. Virtualization encapsulates all the new virtual instances with a new operating system but containerization encapsulates the software only with its dependencies. Containerization avoids the problem of dependency missing between different operating systems and their distributions. The concept of containerization is old but the development of open-source tools like Dock
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17

Avolio, Giuseppe, Mattia Cadeddu, and Reiner Hauser. "Evaluating Kubernetes as an orchestrator of the Event Filter computing farm of the Trigger and Data Acquisition system of the ATLAS experiment at the Large Hadron Collider." EPJ Web of Conferences 214 (2019): 07024. http://dx.doi.org/10.1051/epjconf/201921407024.

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The ATLAS experiment at the LHC relies on a complex and distributed Trigger and Data Acquisition (TDAQ) system to gather and select particle collision data. The Event Filter (EF) component of the TDAQ system is responsible for executing advanced selection algorithms, reducing the data rate to a level suitable for recording to permanent storage. The EF functionality is provided by a computing farm made up of thousands of commodity servers, each executing one or more processes. Moving the EF farm management towards a solution based on software containers is one of the main themes of the ATLAS TD
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18

Ermolenko, D., K. Kilicheva, A. Khakimov, and A. Muthanna. "Exploring a Model Network for Orchestation IoT Services Based on Kubernetes." Telecom IT 8, no. 4 (2020): 69–82. http://dx.doi.org/10.31854/2307-1303-2020-8-4-69-82.

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This paper focuses on the implementation of the concept of microservices, which can be used to design and implement distributed edge services for IoT applications. Basically, IoT is a platform where integrat-ed services are connected to a common network, so all devices are able to collect and exchange data with each other. Typically, monolithic user mobility services are developed 
 for the core components of the ETSI MEC Unified Systems Reference Architecture. The ETSI MEC views microservices as a tool for separating monolithic applications into a set of loosely coupled distributed compo
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19

Emami Khoonsari, Payam, Pablo Moreno, Sven Bergmann, et al. "Interoperable and scalable data analysis with microservices: applications in metabolomics." Bioinformatics 35, no. 19 (2019): 3752–60. http://dx.doi.org/10.1093/bioinformatics/btz160.

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Abstract Motivation Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. Results We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for
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20

Vithlani, Marcel, Melville, et al. "Applicability of Remote Sensing Workflow in Kubernetes-Managed On-premise Cluster Environment." Proceedings 30, no. 1 (2019): 38. http://dx.doi.org/10.3390/proceedings2019030038.

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The acquisition, storage, and processing of huge amounts of data and their fast analysis to generate information is not a new approach, but it becomes challenging through smart decision-making on the choice of hardware and software improvements. In the specific cases of environment protection, nature conservation, and precision farming, where fast and accurate reactions are required, drone technologies with imaging sensors are of interest in many research groups. However, post-processing of the images acquired by drone-based sensors such as the generation of orthomosaics from aerial images and
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Bagnasco, Stefano, Gabriele Gaetano Fronzé, Federica Legger, Stefano Lusso, and Sara Vallero. "Delivering a machine learning course on HPC resources." EPJ Web of Conferences 245 (2020): 08016. http://dx.doi.org/10.1051/epjconf/202024508016.

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In recent years, proficiency in data science and machine learning (ML) became one of the most requested skills for jobs in both industry and academy. Machine learning algorithms typically require large sets of data to train the models and extensive usage of computing resources, both for training and inference. Especially for deep learning algorithms, training performances can be dramatically improved by exploiting Graphical Processing Units (GPUs). The needed skill set for a data scientist is therefore extremely broad, and ranges from knowledge of ML models to distributed programming on hetero
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Costa, Raquel L., Luiz Gadelha, Marcelo Ribeiro-Alves, and Fábio Porto. "GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis." PeerJ 5 (July 5, 2017): e3509. http://dx.doi.org/10.7717/peerj.3509.

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There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. In
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