Docker Containers Research Articles

Eğitim ve Araştırma Amaçlı Sanal Robotik Laboratuvarı ve Materyallerinin Geliştirilmesi

Robotic era is near, has never been before. In this new era, robotic education must have quality and accessibility for everyone. In this study, a disruptive technology for future of distance education based on virtual robotic laboratory (U-Plat) is presented. The created robotics education platform allows users to access a virtual robotics laboratory and training programs using internet interfaces. This environment allows them to control simulated robots, analyze and evaluate the results of experiments performed. U-Plat gives researchers, students and people working in the field of robotics the chance to learn Robot Operating System (ROS) and increase their knowledge with robotics training from beginner to advanced level. Also, U-Plat gives researchers opportunity to try their codes in online platform. Compared to traditional learning methods, U-Plat can be seen as an easier and more effective learning/teaching platform for ROS exercises, as it is an open source system. The user experience has been tried to be provided in all training tools with a very simplified and easily understandable interface. All designed training tools are provided through a web interface supported by advanced cloud systems and Docker container infrastructure. As a result, U-Plat is a novel robotic learning platform that can lead future laboratory research and distance robotic education.

Container Load Placement for Deep Learning Application Using Whale Optimization

The deployment and scaling of deep learning applications in distributed computing environments pose significant challenges, particularly in the context of containerized virtualization. Efficient placement and management of Docker containers are critical to optimizing resource utilization, minimizing latency, and ensuring the scalability of deep learning models across clusters of machines. In this paper, we will compare five methods of container placement that are implemented within a scheduling method named Differentiate Quality of Experience Scheduling (DQoES). The container placement methods to be compared include the default Docker Swarm container placement, Discrete Whale Optimization Container Placement (DOWCP), proposed whale optimization, a proposed hybrid with DWOCP, and a proposed hybrid with proposed whale optimization. Based on the experimental results, the method that demonstrates better performance than both the default Docker Swarm container placement and DWOCP is the proposed hybrid with proposed whale optimization.

Evaluation of a Long-Distance IEEE 802.11ah Wireless Technology in Linux Using Docker Containers

Wireless technologies are essential for modern people to maintain uninterrupted connection to the Internet. The most popular standards for wireless technologies are standards of the IEEE 802.11 family. Currently, data transmission rate achievable by IEEE 802.11ac or 802.11ax standards can reach up to 10 Gbit/s. Different IEEE standards have specific data transmission rates. For example, the IEEE 802.11ah standard or Wi-Fi HaLow (code name) operates in the 900 MHz band, which is an unlicensed frequency band below 1 GHz, and is called the “Sub-1-GHz” range. In theory, this standard can provide coverage range of up to 543 meters indoors and data transfer rate of up to 347 Mbit/s (using a maximum of four spatial streams and 16 MHz channel bandwidth). The great benefit of the 802.11ah standard is low energy consumption, which enables communication between devices from the Internet of Things (IoT) over long distances without using a lot of energy. The Wi-Fi HaLow standard is being studied by the authors of the presented article in the ns-3 network simulation program with the 802.11ah module installed and implemented in Docker containers, VirtualBox Virtual Machines (VMs) with a running Linux operating system. During the simulations, results were obtained for the Docker containers simulation with a limited number of stations over different simulation times. These results have been studied in different scenarios. In the scenarios, the results of the Wi-Fi HaLow network simulation were converted into another simulation time, and thus were compared with each other.

DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data.

De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major challenge due to sequence errors, uneven coverage, and mapping artifacts. Here, we developed a deep convolutional neural network (CNN) DNM caller (DeNovoCNN), that encodes the alignment of sequence reads for a trio as 160n}{} times164 resolution images. DeNovoCNN was trained on DNMs of 5616 whole exome sequencing (WES) trios achieving total 96.74% recall and 96.55% precision on the test dataset. We find that DeNovoCNN has increased recall/sensitivity and precision compared to existing DNM calling approaches (GATK, DeNovoGear, DeepTrio, Samtools) based on the Genome in a Bottle reference dataset and independent WES and WGS trios. Validations of DNMs based on Sanger and PacBio HiFi sequencing confirm that DeNovoCNN outperforms existing methods. Most importantly, our results suggest that DeNovoCNN is likely robust against different exome sequencing and analyses approaches, thereby allowing the application on other datasets. DeNovoCNN is freely available as a Docker container and can be run on existing alignment (BAM/CRAM) and variant calling (VCF) files from WES and WGS without a need for variant recalling.

EXNVerify: coverage analysis for long and short-read sequencing data in clinical context.

Accurate identification of genetic variants to a large extent is based on the type of experimental technology, quality of the material and coverage of sequencing data obtained. The latter, coverage quality, highly influences variant calling accuracy and final diagnosis. Our motivation was to create a tool that will evaluate genome coverage and accelerate the introduction of long-read sequencing to medical diagnostics and clinical practice. The implementation was guided by the ease of use of the tool by users who are not proficient in using complex software. A Docker container is perfect for this purpose. Using Docker's advantages (flexibility, mobility and ease of use of the proposed tools), we created eXNVerify. This is a tool for inspection of clinical data in the context of pathogenic variants search. The tool calculates clinical depth coverage (CDC) - a measure of coverage which we introduce to evaluate loci with pathogenic germline and somatic variants reported in ClinVar. The tool additionally provides visualization options for user-defined genes of interest. Finally, we present examples of BRCA1, TP53, CFTR application and results of a test conducted in the Extensive Sequence Dataset of Gold-Standard Samples for Benchmarking and Development. eXNVerify improves the diagnostic process of patients related to important genetic diseases and facilitates the assessment of genetic samples by diagnosticians. The use of Docker allows to run an analysis package and does not require any special technical preparation. Detailed examples are included in the GitHub project documentation and the package can be downloaded directly from DockerHub using the command: docker pull porebskis/exnverify:1.0.

Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

AbstractThe challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet‐of‐things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low‐cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon‐based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.

Toward Future Internet of Things Experimentation and Evaluation

The Internet of Things (IoT) and its current Internet engineering task force and IEEE dual stack model have been facing limitations, requiring applications to overcome tough constraints by their own, narrowing the full potential of this concept. For instance, the IoT dual stack model does not entirely address or support perennial naming, immutable data provenance, data accountability, mobility support, and services’ self-organization. On the other hand, future Internet architecture (FIA) research emerges as a viable candidate to compensate these gaps through evolutionary and revolutionary efforts. Regardless of the FIA proposal model, this field lacks a suitable environment that enables the performance experimentation and evaluation of distinct protocols and architectures fairly and transparently. This article proposes a novel IoT experimentation and evaluation environment based on a low-cost and open-source solution. The proposed environment integrates technologies of containers, IoT nodes emulation, and network simulation to evaluate the current IoT dual stack and a promising FIA approach called eXpressive Internet architecture. Moreover, this work combines the mentioned disjointed technologies, exploiting Docker containers, Contiki open-source operational system for IoT, and Cooja the IoT network simulator. In this way, it addresses the objective of fostering an honest experimentation and evaluation environment for FIAs. Experimental results demonstrate the feasibility and effectiveness of this approach, broadening this field and sowing a path to work with other FIAs.

Implementing a Microservices Architecture for Predicting the Opinion of Twitter Users on COVID Vaccines.

A strong trend in the software industry is to merge the activities of deployment and operationalization through the DevOps approach, which in the case of artificial intelligence is called Machine Learning Operations (MLOps). We present here a microservices architecture containing the whole pipeline (frontend, backend, data predictions) hosted in Docker containers which exposes a model implemented for opinion prediction in Twitter on the COVID vaccines. This is the first description in the literature of implementing a microservice architecture using TorchServe, a library for serving Pytorch models.

PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures.

Knowledge of protein-ligand binding sites (LBSs) enables research ranging from protein function annotation to structure-based drug design. To this end, we have previously developed a stand-alone tool, P2Rank, and the web server PrankWeb (https://prankweb.cz/) for fast and accurate LBS prediction. Here, we present significant enhancements to PrankWeb. First, a new, more accurate evolutionary conservation estimation pipeline based on the UniRef50 sequence database and the HMMER3 package is introduced. Second, PrankWeb now allows users to enter UniProt ID to carry out LBS predictions in situations where no experimental structure is available by utilizing the AlphaFold model database. Additionally, a range of minor improvements has been implemented. These include the ability to deploy PrankWeb and P2Rank as Docker containers, support for the mmCIF file format, improved public REST API access, or the ability to batch download the LBS predictions for the whole PDB archive and parts of the AlphaFold database.

Smart Vision Enabled Low Cost Autonomous Robot

Need for security and human resources for monitoring are growing nowadays. Current security systems are static in nature, lack analysis and prone to several threats and attacks. Alternative proctoring robots are hardwired, cannot reuse and expensive to implement making it unfit for Industrial and Public uses. Therefore, we propose a low-cost, re-configurable autonomous robot system for security and monitoring purposes. The robot system architecture is inspired from cloud data centre architecture where the applications are sandboxed and virtualized for efficient utilization of resources. The AI Model, Source Code, Executable scripts, internal resources are contained as a docker container. These containers are called as modules which are connected in a loosely coupled format. Modules can be replaced, added, deleted, updated, scaled over within the robot. Modules are classified as functional and auxiliary where functional modules performs AI operations, analysis and auxiliary modules performs remote results streaming, recording data also backing up footage and data to private or public cloud. The autonomous machine’s camera is virtualized for simultaneous camera access by modules and to reduce computational overhead. As the resource utilization is optimized the power consumption is also reduced with combined efficiency of ARM and RISC-V chipsets. Thus, with this configurable, power efficiency, autonomous robot we hope to improve the quality of life and standards in public and industrial work places.

Finite element approximation and analysis of a viscoelastic scalar wave equation with internal variable formulations

We consider linear scalar wave equations with a hereditary integral term of the kind used to model viscoelastic solids. The kernel in this Volterra integral is a sum of decaying exponentials (The so-called Maxwell, or Zener model) and this allows the introduction of one of two types of families of internal variables, each of which evolve according to an ordinary differential equation (ODE). There is one such ODE for each decaying exponential, and the introduction of these ODEs means that the Volterra integral can be removed from the governing equation. The two types of internal variable are distinguished by whether the unknown appears in the Volterra integral, or whether its time derivative appears; we call the resulting problems the displacement and velocity forms. We define fully discrete formulations for each of these forms by using continuous Galerkin finite element approximations in space and an implicit ‘Crank–Nicolson’ type of finite difference method in time. We prove stability and a priori bounds, and using the FEniCS environment, https://fenicsproject.org/ (The FEniCS project version 1.5, Archive of Numerical Software, 3 (100), 9–23, 2015.) give some numerical results. These bounds do not require Grönwall’s inequality and so can be regarded to be of high quality, allowing confidence in long time integration without an a priori exponential build up of error. As far as we are aware this is the first time that these two formulations have been described together with accompanying proofs of such high quality stability and error bounds. The extension of the results to vector-valued viscoelasticity problems is straightforward and summarised at the end. The numerical results are reproducible by acquiring the python sources from https://github.com/Yongseok7717, or by running a custom built docker container (instructions are given).

Integration With Docker Container Technologies for Distributed and Microservices Applications

Docker containerization is a newly emerging technology, which brings virtualization to software applications. Specifically, lightweight-ness has brought higher advantage for the Docker containers. Often, the deployment of distributed net centric applications on cloud hosted Docker containers is a new approach for enterprise applications. This emerging approach has been discussed on many online forums, but currently there is no secondary research study to consolidate this approach. This research study aims to identify, systematically compare and taxonomically classify the existing research studies and their practical approaches. The systematic study was conducted of fifty-seven (57) selected research studies. Selected research studies were classified and compared based on the characterization framework. The study brings the results in a discussion of emerging concerns in the Docker based deployments. Furthermore, the study is moving closer to current concerns in distributed, microservices based net centric software applications

Light weight serverless computing at fog nodes for internet of things systems

Internet of <span lang="EN-US">things (IoT) systems collect large size of data from huge numbers of sensors. A wide rage of IoT systems relies on cloud resources to process and analyze the collected data. However, passing large amount of data to the cloud affects the overall performance and cannot support real-time requirements. Serverless computing is a promising technique that allows developer to write an application code, in any programming language, and specify an event to start its execution. Thus, IoT system can get a good benefit of serverless environment. The proposed work introduces a framework to allow Serverless computing to take place on the Fog nodes near the data collectors. The proposed framework is implemented as an extension to a Kubernetes cluster that manages a set of Docker containers at the fog layer. A prototype of the proposed solution was implemented using Node.Js for coding and YAML files to transfer data. The proposed framework was evaluated against traditional cloud Serverless execution. The experimental results proved the significant enhancement of the framework by dcreasing the respond time especially for data intensive IoT applications.</span>

NGOME-Lite: Proteome-wide prediction of spontaneous protein deamidation highlights differences between taxa.

Asparagines in proteins deamidate spontaneously, which changes the chemical structure of a protein and often affects its function. Current prediction algorithms for asparagine deamidation require a structure as an input or are too slow to be applied at a proteomic scale. We present NGOME-Lite, a new version of our sequence-based predictor for spontaneous asparagine deamidation that is faster by over two orders of magnitude at a similar degree of accuracy. The algorithm takes into account intrinsic sequence propensities and slowing down of deamidation by local structure. NGOME-Lite can run in a proteomic analysis mode that provides the half-time of the intact form of each protein, predicted by taking into account sequence propensities and structural protection or sequence propensities only, and a structure protection factor. The detailed analysis mode also provides graphical output for all Asn residues in the query sequence. We applied NGOME-Lite to over 257,000 sequences in 38 proteomes and found that different taxa differ in their predicted deamidation dynamics. Spontaneous protein deamidation is faster in Eukarya than in Bacteria because of a higher degree of structural protection in the latter. Predicted protein deamidation half-lifes correlate with protein turnover in human, mouse, rat, C. elegans and budding yeast but not in two plants and two bacteria. NGOME-Lite is implemented in a docker container available at https://ngome.proteinphysiologylab.org.

Design Scheme of a Docker Container File Isolation against Computer Virus Spreading

With the spread of computers and Internet of Things, the destructive power of viruses gradually becomes stronger, which threaten the daily life of people. In order to effectively restrain the virus spreading and reduce economic losses, humans need to hold the law and control of the virus distribution. Docker has obtained significant attention as a lightweight virtualization container technology. Compared to traditional virtualization technologies, docker possesses the advantages of fast deployment, low resource consumption, and high migration capability. Given that the differences between various virtualization technologies and combining the characteristics in electric power systems, this paper designs a scheme of the file isolation system based on docker containers. The isolation scheme could reduce the highly dependence on the underlying operating system in the current application deployment of electric power systems and facilitate the security access to the file system between containers.

DBSP_DRP: A Python package for automated spectroscopic data reduction of DBSP data

DBSP_DRP is a python package that provides fully automated data reduction of data taken by the Double Spectrograph (DBSP) at the 200-inch Hale Telescope at Palomar Observatory (Oke & Gunn, 1982). The underlying data reduction functionality to extract 1D spectra, perform flux calibration and correction for atmospheric absorption, and coadd spectra together is provided by PypeIt (Prochaska et al., 2020). The new functionality that DBSP_DRP brings is in orchestrating the complex data reduction process by making smart decisions so that no user input is required after verifying the correctness of the metadata in the raw FITS files in a table-like GUI. Though the primary function of DBSP_DRP is to automatically reduce an entire night of data without user input, it has the flexibility for astronomers to fine-tune the data reduction with GUIs for manually identifying the faintest objects, as well as exposing the full set of PypeIt parameters to be tweaked for users with particular science needs. DBSP_DRP also handles some of the occasional quirks specific to DBSP, such as swapping FITS header cards, adding (an) extra null byte/s to FITS files making them not conform to the FITS specification, and not writing the coordinates of the observation to file. Additionally, DBSP_DRP contains a quicklook script for making real-time decisions during an observing run, and can open a GUI displaying a minimally reduced exposure in under 15 seconds. Docker containers are available for ease of deploying DBSP_DRP in its quicklook configuration (without some large atmospheric model files) or in its full configuration.

Con-Pi: A Distributed Container-Based Edge and Fog Computing Framework

Edge and Fog computing paradigms overcome the limitations of cloud-centric execution for different latency-sensitive Internet of Things (IoT) applications by offering computing resources closer to the data sources. Small single-board computers (SBCs) like Raspberry Pis (RPis) are widely used as computing nodes in both paradigms. These devices are usually equipped with moderate speed processors and provide support for peripheral interfacing and networking, making them well-suited to deal with IoT-driven operations such as data sensing, analysis, and actuation. However, these small Edge devices are constrained in facilitating multi-tenancy and resource sharing. The management of computing and peripheral resources through centralized entities further degrades their performance and service quality significantly. To address these issues, a fully distributed framework, named Con-Pi, is proposed in this work to manage resources at the Edge or Fog environments. Con-Pi exploits the concept of containerization and harnesses Docker containers to run IoT applications as micro-services. %Moreover, Con-Pi operates in a distributed manner across multiple RPis and enables them to share resources. The software system of the proposed framework also provides a scope to integrate different IoT applications, resource and energy management policies for Edge and Fog computing. Its performance is compared with the state-of-the-art frameworks through real-world experiments. The experimental results show that Con-Pi outperforms others in enhancing response time and managing energy usage and computing resources through its distributed offloading model. Further, we have developed an automated pest bird deterrent system using Con-Pi to demonstrate its suitability in developing practical solutions for various IoT-enabled use cases, including smart agriculture.

Selenium-Jupiter: A JUnit 5 extension for Selenium WebDriver

Selenium WebDriver is a library that allows controlling web browsers (e.g., Chrome, Firefox, etc.) programmatically. It provides a cross-browser programming interface in several languages used primarily to implement end-to-end tests for web applications. JUnit is a popular unit testing framework for Java. Its latest version (i.e., JUnit 5) provides a programming and extension model called Jupiter. This paper presents Selenium-Jupiter, an open-source JUnit 5 extension for Selenium WebDriver. Selenium-Jupiter aims to ease the development of Selenium WebDriver tests thanks to an automated driver management process implemented in conjunction with the Jupiter parameter resolution mechanism. Moreover, Selenium-Jupiter provides seamless integration with Docker, allowing the use of different web browsers in Docker containers out of the box. This feature enables cross-browser testing, load testing, and troubleshooting (e.g., configurable session recordings). This paper presents an example case in which Selenium-Jupiter is used to evaluate the performance of video conferencing systems based on WebRTC. This example case shows that Selenium-Jupiter can build and maintain the required infrastructure for complex tests effortlessly.

Zero Trust Container Architecture (ZTCA)

Containerisation is quickly becoming an accepted industry standard for development environments and Gartner, in a recent market forecast, estimated that by 2022 more than 75% of organisations will be using containers in production deployments. With this explosion in growth comes an added focus on security and best practices for using containers. The use of containers, in particular Docker containers, has altered some of the more traditional deployment paradigms by giving control of deployments to the development teams. This has massively benefited the DevOps release cycle, but at the expense of many mature security and review processes that are integrated into traditional deployments. Like all systems, containers need frameworks to guide best practices for deployments and to ensure mistakes are not made that increase the risk level or attack surface of an application or service using containers, or the containers themselves. Indeed, according to a recent presentation during DevSecCon24 by Justin Cormack, Security Lead at Docker Inc., Cormack believes most security issues related to Docker are due to misconfiguration rather than direct exploit. While work has been previously conducted with regards to container security and separately applying Zero Trust Networking Architecture to containers, in this work we will investigate the security state of a default deployment of the Docker container engine on Linux and analyse how the principals of Zero Trust Architecture can be extended beyond the domain of networking, distilled into a ”Zero Trust Containers Architecture” and applied to secure Docker deployments. In order to determine this, research was conducted into the current state of Docker security and Zero Trust Architecture. Practical and theoretical attacks were reviewed against a default Docker deployment to identify common themes and areas of issue. Results were used to advise a generalised trust-based framework which was then used to analyse a Docker deployment and validate mitigation of a selection of the identified attacks, proving out the concept of the proposed “Zero Trust Container Architecture” framework.

Secure PAAS environment over hybrid cloud using load-balanced Docker containers

The purpose of this research is to provide cloud service providers (CSP) a secure platform that can be accessed for configuring secure, silent software deployment on the hybrid cloud using load-balanced Docker Containers (LBDC) that are configured with different PAAS software solutions. As many researchers in the past have worked on providing PASS platforms using either a public cloud or private cloud less research is carried out on the hybrid cloud systems and researchers who have worked on the hybrid cloud have followed a different approach to providing PAAS to organizations in need but lack in securing the PAAS platform. This research will provide cloud service providers a secure deployment of the PAAS platform for the developers on the go and will reduce the overhead of CSP to deploy the PAAS services from scratch. Compared to the other researcher’s contribution this research work provides a load-balanced containerized secure PASS platform that can be scaled on-demand and be secure while scaling the resources for peak workloads. The admins of the hybrid cloud can access and deploy the Docker containers based on the requirement laid down by the developers. Once the Admin configures the Docker container with the required PAAS services he can then easily invoke the LBDC in the hybrid cloud and deploy the PAAS platform for the developers to work on in seconds. LBDC’s will help in scalable deployment of Secure PAAS services for different locations and machines simultaneously thus reducing the cost and time required for hybrid virtual machines to get up and running for the development process by the developers of the company. CSP’s can achieve less turnaround time for deployment of secure PAAS environments for organizations.