Azure Cloud Platform Research Articles

STUDY OF METHODS OF CREATING SERVICE-ORIENTED SOFTWARE SYSTEMS IN AZURE

The modern development of service-oriented software systems is accompanied by the wide use of cloud technologies, which affect the competitiveness of companies and their systems, which provide opportunities to expand the client base thanks to the coverage of several regions of the city or country.
 The advantage of cloud services is availability in any part of the world where there is an Internet connection. Cloud providers provide a large volume of services for various needs: such as hosting, deployment of containers, file storage, databases, etc.
 In particular, all the most popular cloud providers offer several options for creating service-oriented software systems, including both standard technologies and proprietary developments. This paper compares the methods of creating service-oriented software systems based on the Azure cloud platform: Azure Container Apps, Azure Kubernete Service, and Azure Red Hat OpenShift. The subject area of technologies for the implementation of service-oriented application architecture is considered, and criteria for the analysis of methods for implementing applications with such an architecture are proposed. A software solution for comparing methods of creating service-oriented applications based on the Azure cloud platform was designed and developed. The developed software system provides an opportunity to rent scooters, bicycles and cars. 
 The purpose of the study is a comparative analysis of the methods of creating service-oriented software systems based on Azure services, and the subject of the study is a software solution implemented using these methods.
 The purpose of this work will be the development of a software system that will provide an opportunity to rent scooters, bicycles and cars. Using this system, we will investigate the deployment of this system on certain services from Azure.
 The results of this research on Azure services: Azure Container Apps, Azure Kubernete Service and Azure Red Hat OpenShift can be used when creating a new software system, when expanding an existing software system, when transferring software system components from other platforms to the Azure platform using these services.

Development and Validation of Innovative Machine Learning Models for Predicting Date Palm Mite Infestation on Fruits

The date palm mite (DPM), Oligonychus afrasiaticus (McGregor), is a key pest of unripe date fruits. The detection of this mite depends largely on the visual observations of the webs it produces on the green fruits. One of the most important problems of DPM control is the lack of an accurate decision-making approach for monitoring and predicting infestation on date fruits. Therefore, this study aimed to develop, evaluate, and validate prediction models for DPM infestation on fruits based on meteorological variables (temperature, relative humidity, wind speed, and solar radiation) and the physicochemical properties of date fruits (weight, firmness, moisture content, total soluble solids, total sugar, and tannin content) using two machine learning (ML) algorithms, i.e., linear regression (LR) and decision forest regression (DFR). The meteorological variables data in the study area were acquired using an IoT-based weather station. The physicochemical properties of two popular date palm cultivars, i.e., Khalas and Barhee, were analyzed at different fruit development stages. The development and performance of the LR and DFR prediction models were implemented using Microsoft Azure ML. The evaluation of the developed models indicated that the DFR was more accurate than the LR model in predicting the DPM based on the input variables, i.e., meteorological variables (R2 = 0.842), physicochemical properties variables (R2 = 0.895), and the combination of both meteorological and the physicochemical properties variables (R2 = 0.921). Accordingly, the developed DFR model was deployed as a fully functional prediction web service into the Azure cloud platform and the Excel add-ins. The validation of the deployed DFR model showed that it was able to predict the DPM count on date palm fruits based on the combination of meteorological and physicochemical properties variables (R2 = 0.918). The deployed DFR model by the web service of Azure Ml studio enhanced the prediction of the DPM count on the date fruits as a fast and easy-to-use approach. These findings demonstrated that the DFR model using Azure Ml Studio integrated into the Azure platform can be a powerful tool in integrated DPM management.

Use of structure-guided 3D controlled-source electromagnetic inversion to map karst features in carbonates in offshore northwest Borneo

Fractured and/or karstic carbonates (FKCs) in the subsurface are commonly associated with significant drilling risk or reservoir effectiveness issues. Understanding the distribution of karsts will help avoid drilling into possible voids and also reassess risks associated with reservoir effectiveness. A 2020 3D seismic acquisition in offshore northwest Borneo provided data for identifying new carbonate prospects and an opportunity to revisit a 2010 marine controlled-source electromagnetic (CSEM) survey that was not designed specifically for FKC mapping but can serve to test new inversion capability for imaging karsted carbonate bodies. A 3D CSEM data were used to detect and characterize the nature (i.e., fractured and/or karsted) of carbonate prospects mapped using 3D seismic data. In one approach, structure tensors computed from these seismic data were used to constrain the 3D CSEM inversion on the Azure cloud platform. In another approach, cross gradients between vertical and horizontal resistivities were used as a structural guide obviating the need for seismic data. The resulting anisotropic resistivity models were validated using resistivity logs from a nearby well; the resistivity results matched the well-logs satisfactorily, providing geologic justification to interpret the presence of karst features in the resistivity volumes. Lateral boundary of the carbonate prospects was mapped based on the resistivity characteristic, in seismic- and nonseismic-guided structure-coupled inversion. Resistivity-depth cross section of the area revealed the presence of resistivity discontinuity at the seismic-inferred boundary of the main carbonate prospect. Also, resistivity depth slices of the models clearly demonstrated that the areal distribution of low and high resistive zones can be mapped and useful deductions such as the presence of FKC and basement characteristics can be made using the 3D inversion of the CSEM data.

Development and deployment of a digital twin for monitoring of an adaptive clamping mechanism, used for high performance composite machining

In this work, we present a cloud-based digital twin for monitoring of a clamping technology for machining of composite parts. Supporting large and/or freeform composite parts is crucial to avoid bending during drilling. Bending of the part will lead to delamination and frayed edges of the drilled holes. The new active clamping technology allows to realise a stabilised fixture, localised in the area where the drilling occurs, to avoid bending. This significantly improves the quality of the drilled holes. The clamping device is equipped with an IoT edge device, with a bidirectional communication to the cloud. The cloud-based digital twin analyses the quality of the drilled holes based on computer vision, monitors the drill wear and detects incorrect operation of the active clamping device. All data is stored in the cloud. By means of a knowledge graph, which acquires and integrates information into an ontology and provides a central information access, it will be easier for a data scientist to query this data and to gain new insights in the operation of the drill with active clamping device. The full deployment occurs on the Microsoft Azure cloud platform. This transforms the standard machine into an Industry 4.0 compliant machine.

PERFORMANCE STUDY OF THE DTU MODEL FOR RELATIONAL DATABASES ON THE AZURE PLATFORM

When solving problems of working with relational databases on cloud platforms, the problem arises of choosing a specific model to ensure the performance of executing queries of varying complexity. The object of research is the processes of implementing various types of queries to relational databases within the framework of the DTU purchase model of the MS Azure platform. The subject is methods for evaluating the performance of work with relational databases based on the timing of query execution and indicators of the load on the resources of the cloud platform. The aim of the study is to develop a system of indicators for monitoring the current state of work with the database for reasonable decision-making on the choice of a certain price category of the DTU model of the MS Azure cloud service, which will optimize the results of working with the database. platforms Achieving the set goals involves the following tasks: to analyze modern tools and services for working with databases, in particular relational databases, on Azure and AWS cloud platforms, the features of their application and implementation; develop software for generating test relational databases of different sizes; test the generated databases on a local resource; taking into account the characteristics of the levels of the Azure DTU model, develop a new system of performance indicators, which includes 2 groups - time indicators and indicators of the load on existing platform resources; develop and implement queries of varying complexity for the generated test database for different levels of the DTU model and analyze the results. Methods. The following methods were used in the research: methods of relational database design; methods of creating queries in SQL-oriented databases with any number of tables; methods of creating and migrating data to cloud platforms; methods of monitoring the results of queries based on time and resource indicators; methods of generating test data for relational databases; system approach for complex assessment and analysis of productivity of work with relational databases. Results. On the basis of the developed scorecard used for the current analysis of the processes of working with relational databases of the MS Azure platform, numerous experiments were carried out for different levels of the model for simple and complex queries to a database with a total volume of 20 GB: loading of DTU model levels when executing various queries, the influence of model levels DTU Azure SQL database on the performance of simple and complex queries, the dependence of the execution time of various queries on the load of the CPU and the speed of write/read operations for different levels of the model. Conclusions. The results of the experiments allow us to conclude that the levels of the DTU model - S3 and S7 - are used to generate test data of various sizes (up to 20 GB) and execute database queries. The practical use of the proposed indicators to evaluate the results of applying the DTU model will improve the efficiency of decision-making on choosing the model level when implementing various queries and generating test data on the Azure cloud platform. The developed set of indicators for working with relational databases on the Azure cloud platform expands the basis of the methodological framework for evaluating the performance of working with relational databases on cloud platforms by analyzing the results of executing the simple and complex database queries on the resources involved.

Migration of a Research Library's ICT-Based Services to a Cloud Platform

Libraries have been at the forefront in adopting emerging technologies to manage the library’s operations and provide information services to the user community they serve. With the emergence of cloud computing (CC) technology, libraries are exploring and adopting CC service models to make their own services more efficient, reliable, secure, scalable, and cost-effective. In this article, the authors share their experience migrating some of the library’s locally hosted ICT-based services onto the Microsoft Azure cloud platform. The migration of services to a cloud platform has helped the library significantly reduce the downtime of its services due to power or network or system outages.

An efficient and robust parallel scheduler for bioinformatics applications in a public cloud: A bigdata approach

<p>In bioinformatics, genomic sequence alignment is a simple method for handling and analysing data, and it is one of the most important applications in determining the structure and function of protein sequences and nucleic acids. The basic local alignment search tool (BLAST) algorithm, which is one of the most frequently used local sequence alignment algorithms, is covered in detail here. Currently, the NCBI's BLAST algorithm (standalone) is unable to handle biological data in the terabytes. To address this problem, a variety of schedulers have been proposed. Existing sequencing approaches are based on the Hadoop MapReduce (MR) framework, which enables a diverse set of applications and employs a serial execution strategy that takes a long time and consumes a lot of computing resources. The author, improves the BLAST algorithm based on the BLAST-BSPMR algorithm to achieve the BLAST algorithm. To address the issue with Hadoop's MapReduce framework, a customised MapReduce framework is developed on the Azure cloud platform. The experiment findings indicate that the suggested bulk synchronous parallel MapReduce-basic local alignment search tool (BSPMR-BLAST) algorithm matches bioinformatics genomic sequences more quickly than the existing Hadoop-BLAST method, and that the proposed customised scheduler is extremely stable and scalable.</p>

A cloud-based digital twin for monitoring of an adaptive clamping mechanism used for high performance composite machining

In this work, we present a cloud-based digital twin for monitoring of a clamping technology for machining of composite parts. Supporting large and/or freeform composite parts is crucial to avoid bending during drilling. Bending of the part will lead to delamination and frayed edges of the drilled holes. The new active clamping technology allows to realize a stabilized fixture, localized in the area where the drilling occurs, to avoid bending. This significantly improves quality of the drilled holes. The clamping device is equipped with an IoT edge device, with a bidirectional communication to the cloud. The cloud-based digital twin analyses the quality of the drilled holes based on computer vision, monitors the drill wear and detects incorrect operation of the active clamping device. All data is stored in the cloud to gain new insights in the operation of the drill with active clamping device. The full deployment occurs on the Microsoft Azure cloud platform. This transforms the standard machine into an Industry 4.0 compliant machine.

BlockchainBus: A lightweight framework for secure virtual machine migration in cloud federations using blockchain

AbstractCloud federations offer numerous advantages such as improved resilience, improved performance, and the prevention of vendor lock‐in. In order to function efficiently, however, they must be supported by automated solutions to deploy cloud resources securely and efficiently. In particular, a system which allows virtual machine (VM) migration from one cloud to another while recording this for charging and security purposes is particularly useful. This paper presents a lightweight framework called BlockchainBus which offers secure VM migration in cloud federations using blockchain. This framework is a VM migration Blockchain ledger which can be used to record these migrations. BlockchainBus framework is implemented using the HyperLedger solution and deployed on the Microsoft Azure cloud platform. The overhead of this system is then evaluated. The performance of the BlockchainBus framework was compared with the overall VM migration time and was determined as 2.36 seconds. This result is shorter than the overall VM migration time of 5 seconds and indicates that the BlockchainBus gives better performance.

Quantification of the Immune Content in Neuroblastoma: Deep Learning and Topological Data Analysis in Digital Pathology.

We introduce here a novel machine learning (ML) framework to address the issue of the quantitative assessment of the immune content in neuroblastoma (NB) specimens. First, the EUNet, a U-Net with an EfficientNet encoder, is trained to detect lymphocytes on tissue digital slides stained with the CD3 T-cell marker. The training set consists of 3782 images extracted from an original collection of 54 whole slide images (WSIs), manually annotated for a total of 73,751 lymphocytes. Resampling strategies, data augmentation, and transfer learning approaches are adopted to warrant reproducibility and to reduce the risk of overfitting and selection bias. Topological data analysis (TDA) is then used to define activation maps from different layers of the neural network at different stages of the training process, described by persistence diagrams (PD) and Betti curves. TDA is further integrated with the uniform manifold approximation and projection (UMAP) dimensionality reduction and the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm for clustering, by the deep features, the relevant subgroups and structures, across different levels of the neural network. Finally, the recent TwoNN approach is leveraged to study the variation of the intrinsic dimensionality of the U-Net model. As the main task, the proposed pipeline is employed to evaluate the density of lymphocytes over the whole tissue area of the WSIs. The model achieves good results with mean absolute error 3.1 on test set, showing significant agreement between densities estimated by our EUNet model and by trained pathologists, thus indicating the potentialities of a promising new strategy in the quantification of the immune content in NB specimens. Moreover, the UMAP algorithm unveiled interesting patterns compatible with pathological characteristics, also highlighting novel insights into the dynamics of the intrinsic dataset dimensionality at different stages of the training process. All the experiments were run on the Microsoft Azure cloud platform.

Creating Web Application for Organizing Teamwork Online Using Microsoft Azure Cloud Services

Based on the services of the cloud platform Microsoft Azure developed a web application that solves problems that arise when organizing teamwork online. For the first time, the architecture of this type of software system based on Microsoft Azure cloud platform services has been proposed. The architecture is flexible and can be extended by adding new modules. Possibilities for improving the implementation of the software system in the following versions have been identified: expanding the tools of the AGILE methodology by including Scrum approaches; expanding real-time interaction, including receiving notifications and reminders about meetings, editing team pages and KANBAN boards; adding the ability to share files between users; expanding interaction with messages (forward, reply, edit, delete); adding methods for notifications other than email, such as Telegram. The implementation of these additional functions will not affect the developed architecture of the software system.

POS1451 DEVELOPING A TOOL TO MEASURE THE LEARNING CURVE: ENTHESITIS ULTRASOUND IN PSORIASIS ARTHRITIS

Background:Enthesitis is defined as inflammation of the tendon inserting the bone encompassing the adjacent trabecular bone network, the fascia and surrounding soft tissues as the cartilage, the bursa and the fat pad. In enthesitis clinical examination alone is a method with significant limitations in terms of diagnostic accuracy and does not correlate strongly with imaging diagnostics (1,2).Ultrasound (US) is a widely used imaging technique in Rheumatology. But while learning curricula and standardization for joint US are available, other US techniques as vascular-, enthesis- or serosa-US gets usually less attention in rheumatologic US curricula. Though both OMERACT (4) and GRAPPA (3) have recently published qualitative and semi quantitative criteria in enthesitis US, few publications explicitly address the learning curves in these particular domain.Objectives:To describe the development of a tool to measure the learning curve for enthesitis US.Methods:3237 US images of 561 enthesis were obtained by one experienced ultrasonographer (PMA) in B- and color Doppler (CD) mode in longstanding psoriasis arthritis patients of different disease activity. Due to duplicity and poorer image quality 2115 images were eliminated. The remaining fully anonymized 1122 images (561 enthesis) were afterward implemented in a random multiple choice algorithm presenting a B-mode and a CD image of the same enthesis at the same time without timely limitation. Rating follows qualitative GRAPPA criteria as well as semiquantitative OMERACT criteria (3, 4). The enthesitis scoring application was than written in.NET/ C#, TypeScript and ReactJS and is hosted in Azure Cloud platform. The scoring is stored in a database allowing extraction to SPSS for statistical analysis.Results:The interface of the functional program is shown in image 1 (Screenshot). In a next step the program will be presented to different raters of different ultrasound experience (>10 years, 5-10 years, 1-5 years, <1 years). The program will be presented a multitude of times in different order to every rater to adjust for inter-rater reliability. Correlation between raters will be given to depict a learning curve on enthesitis ultrasound assuming the rater with the highest experience as gold standard.Image 1.Screenshot of grafical user interface of the scoring program.Conclusion:In this presentation we outline the successful development of a tool to measure the learning curve in enthesitis. We hypothesize that knowledge about the learning curve and inter-rater reliability in enthesitis US obtained by our tool might contribute to future US curricula, structured reporting and deep learning algorithms.

Intelligent Multi-Keyword Ranked Searchable Security Algorithm

enterprises are moving their valuable information to the cloud because of the advantage that storage as a service brings. Reliability is at the heart of the cloud service and relationship. We use the sense of security when establishing trust. Trust is established through cryptography. Searchable encryption is a method to provide security. Literature research workers have been studying searchable encryption schemes. We will focus on advanced techniques that leverage CRSA and B-Tree to enhance level of trust. We conducted a search on encrypted data using the Azure cloud platform.

Integrated Geo Cloud Solution for Seismic Data Processing

Oil industries generate an enormous volume of digitized data (e.g., seismic data) as a part of their seismic study and move it to the cloud for downstream applications. Moving massive data into the cloud can pose many challenges, especially to Commercial-off-the-shelf geoscience applications as they require very high compute and disk throughput. This paper proposes a digital transformation framework for efficient seismic data processing and storage comprising of: (a) Novel Data storage options, (b) Cloud-based HPC framework for efficient seismic data processing, and (c) MD5 hash calculation using the MapReduce pattern with Hadoop clusters. Azure cloud platform is used to validate the proposed framework and compare it with the existing process. Experimental results show a significant improvement in execution time, throughput, efficiency, and cost. The proposed framework can be used in any domain which deals with extensive data requiring high compute and throughput.

Digitalization of Business Processes of Enterprises of the Ecosystem of Industry 4.0: Virtual-Real Aspect of Economic Growth Reserves

The purpose of the research is to present the features of digitization of business processes in enterprises as a foundation on which the gradual formation of Industry 4.0 and the search for economic growth in new virtual reality, which has every chance to be a decisive step in implementing digital strategy for Ukraine and development of the innovation ecosystem. Key problems that arise during the digitalization of business processes in enterprises are presented, among which are: the historical orientation of production to mass, “running” sizes and large batches; large-scale production load; the complexity of cooperation and logic between production sites. It is determined that high-quality and effective tools of innovation-digital transformation in the conditions of virtual reality should include: a single system of on-line order management for all enterprises (application registration – technical expertise – planning – performance control – shipment); Smart Factory, Predictive Maintenance, IIoT, CRM, SCM. Features of digital transformation in the part of formation of enterprises of the ecosystem of Industry 4.0 are revealed. The capabilities and benefits of using Azure cloud platform in enterprises, which includes more than 200 products and cloud services, are analyzed. Azure is said to support open source technologies, so businesses have the ability to use tools and technologies they prefer and are more useful. After conducting a thorough analysis of the acceleration of deep digitalization of business processes by enterprises, authors proposed to put into practice Aruba solution for tracking contacts in the fight against COVID-19. Aruba technology helps locate, allowing you to implement flexible solutions based on Aruba Partner Ecosystem using a USB interface. It is proposed to use SYNTEGRA – a data integration service that provides interactive analytics and provides data models and dashboards in order to accelerate the modernization of data storage and management, optimize reporting in the company and obtain real-time analytics. The possibilities of using Azure cloud platform during the digitization of business processes of enterprises of the ecosystem of Industry 4.0 in the conditions of virtual reality are determined.

A Novel Framework for Fog Computing: Lattice-Based Secured Framework for Cloud Interface

Interconnection and intercommunication between people, processes, and things have been enhanced with the development of the Internet of Things (IoT) and extended with fog computing for better efficiency. Fog computing improves the network services and circumvents the problem of escalated data management as an interface between cloud and terminal with the requirement of secure data transmission. In this article, a novel security framework is designed for fog computing intended for improving the security of IoT. This framework uses single point of aggregation from fog interface and lattice cryptographic approach to establish the security for the services. Azure cloud platform and Sage lattice cryptographic base have been employed to evaluate the performance of the framework. It is noticed that the Level 1-Level 2 (L1–L2) cache implementation in the system significantly improves the efficiency of the framework. The results show that the proposed framework is robust in preventing attacks on fog nodes and additionally provides an advantage for low communication overhead.

Characterizing network paths in and out of the clouds

Commercial Cloud computing is becoming mainstream, with funding agencies moving beyond prototyping and starting to fund production campaigns, too. An important aspect of any scientific computing production campaign is data movement, both incoming and outgoing. And while the performance and cost of VMs is relatively well understood, the network performance and cost is not. This paper provides a characterization of networking in various regions of Amazon Web Services, Microsoft Azure and Google Cloud Platform, both between Cloud resources and major DTNs in the Pacific Research Platform, including OSG data federation caches in the network backbone, and inside the clouds themselves. The paper contains both a qualitative analysis of the results as well as latency and peak throughput measurements. It also includes an analysis of the costs involved with Cloud-based networking.

Experiential probabilistic assessment of cloud services

Substantial difficulties in adopting cloud services are often encountered during upgrades of existing software systems. A reliable early stage analysis can facilitate an informed decision process of moving systems to cloud platforms. It can also mitigate risks against system quality goals. Towards this, we propose an interactive goal reasoning approach which is supported by a probabilistic layer for the precise analysis of cloud migration risks to improve the reliability of risk control. The approach is illustrated using a commercial scenario of integrating a digital document processing system to Microsoft Azure cloud platform.

A Benchmarking Tool for Elastic MQTT Brokers in IoT Applications

Cloud computing is an evolution in IT consumption and delivery which makes available self-management on the Internet with a flexible, pay-as-you-go business model. Within the context of Internet of Things, the MQTT (Message Queuing Telemetry Transport) protocol that is implemented broadly by the applications of “Publish-Subscribe” paradigm has a vital role. However, MQTT brokers are saturated easily if they have to cope with huge and speedy data generated by IoT “chatty” devices. With capability of provisioning/deprovisioning granular virtual resources, Cloud computing empowered MQTT brokers by enabling its elasticity feature. Elasticity helps the brokers deal with a very large variety of data integrated into the IoT every single day. However, there was lack of sturdy benchmarking tools that judge all the aspects of MQTT brokers in order to advocate correct elastic decision-making. This article focuses on the work of benchmarking MQTT by introducing a new developed tool called MQTTBrokerBench. With this tool, users not only can benchmark MQTT brokers but also can specify saturation points where the IoT load makes the brokers be saturated. Those saturation points can be used to set thresholds for elastic decision-making. Furthermore, the article also demonstrates the results acquired by this tool through the experiments on Windows Azure Cloud Platform.

Design and Implementation of Authentication as a Service (Aaas) in Windows Azure Cloud Platform

Cloud computing is an internet based computing. It consists of various services and delievers on demand basis from the user request. Data security is a necessary facet for any web applications. Authentication is an important security problem in cloud as well as internet facing applications. This paper proposes authentication algorithms for securing the cloud applications. Authentication is an essential factor among other security parameters such as authorization, auditing and availability. These proposed algorithms are used to protect the unauthorized access in cloud. User’s data are converted into different form using the different encryption techniques. These three algorithms are combined and provide as a single service to the cloud user. Proposed User authentication mechanisms are designed and developed in ASP.NET platform and deployed on Windows Azure platform of cloud System. This user authentication mechanism is simply matched with any kind of service within the cloud environment. The proposed user authentication algorithms have provided best security in cloud system. This mechanism can be used for agriculture, medical, education and IT based applications to firmly access the information in secured manner.