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1
Ye, Yan Xin, Jian Ming Cui, and Jian Ming Lui. "Achieving Message Board Function Based on Storage Services of the Windows Azure Platform." Applied Mechanics and Materials 380-384 (August 2013): 2411–14. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2411.
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in order to study the development of the Windows Azure platform, the paper through the use of cloud computing, one of the platforms Windows Azure, using its Table Storage storage services,to realize a message board function; and realize a good combination of NET Framework and Windows Azure, and explore the Difference of the Microsoft Windows Azure cloud computing platform development and the difference between ordinary ASP.NET development.
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Chauhan, Akash. "A Comparative Study of Cloud Computing Platforms." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 1 (April 30, 2020): 821–26. http://dx.doi.org/10.17762/turcomat.v11i1.13563.
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This article offers a comparison and analysis of three of the most widely used cloud computing platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). The research focuses on the most important aspects of each platform, such as the virtual machines they offer, the storage choices they provide, the database services they provide, and the serverless computing capabilities they provide. In addition to this, the paper discusses issues like as cost, availability, security, and scalability, as well as some of the positives and negatives associated with using each platform. The literature review that is included in this paper highlights some of the most important findings from recent studies on cloud computing platforms. These findings include the widespread use of Amazon Web Services (AWS), the difficulties that are encountered by organisations in the management of cloud computing resources, and the significance of aspects such as reliability, security, and cost when selecting a cloud computing platform. This article's overarching objective is to offer businesses a comprehensive introduction to AWS, Microsoft Azure, and Google Cloud Platform (GCP), and to assist those businesses in making well-informed choices regarding which cloud platform is best suited to meet their requirements.
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Amrullah, Agit, Agung Nugroho, and Zekriansyah Ramadhan. "PERBANDINGAN KINERJA WEB SERVER PADA PENYEDIA LAYANAN CLOUD MICROSOFT AZURE DAN AMAZON WEB SERVICES." Jurnal Informatika Teknologi dan Sains 5, no. 1 (February 8, 2023): 92–97. http://dx.doi.org/10.51401/jinteks.v5i1.2487.
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Webserver adalah hal yang sangat penting sebagai layanan yang dibutuhkan agar klien dapat mengakses WWW (World Wide Web) menggunakan web browser mereka. Apache dan nginx adalah software web server yang paling banyak dipakai didunia, selain karena integrasinya yang mudah ke berbagai panel web seperti Cpanel, kedua software ini memiliki kestabilan yang mempuni dalam menanangani permintaan klien. Microsoft Azure dan Amazon Web Services sebagai salah satu penyedia layanan Cloud Computing Software As Service (SaaS) dan Platform As Service (PaaS), memiliki performa yang berbeda untuk implementasi pada web server. Penelitian ini bertujuan dalam melakukan analisa kinerja webserver apache dan nginx pada platform Microsoft Azure dan Amazon Web Services (AWS). Dari analisa yang dilakukan bahwasanya webserver Apache lebih unggul dengan margin persentase rata-rata sebesar 7% diplatform Microsoft Azure dan Nginx lebih unggul di platform Microsoft Azure dengan margin persentase sebesar 8,21%.
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Al-Sayyed, Rizik M. H., Wadi’ A. Hijawi, Anwar M. Bashiti, Ibrahim AlJarah, Nadim Obeid, and Omar Y. A. Al-Adwan. "An Investigation of Microsoft Azure and Amazon Web Services from Users’ Perspectives." International Journal of Emerging Technologies in Learning (iJET) 14, no. 10 (May 30, 2019): 217. http://dx.doi.org/10.3991/ijet.v14i10.9902.
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Cloud computing is one of the paradigms that have undertaken to deliver the utility computing concept. It views computing as a utility similar to water and electricity. We aim in this paper to make an investigation of two highly efficacious Cloud platforms: Microsoft Azure (Azure) and Amazon Web Services (AWS) from users’ perspectives the point of view of users. We highlight and compare in depth the features of Azure and AWS from users’ perspectives. The features which we shall focus on include (1) Pricing, (2) Availability, (3) Confidentiality, (4) Secrecy, (5) Tier Account and (6) Service Level Agreement (SLA). The study shows that Azure is more appropriate when considering Pricing and Availability (Error Rate) while AWS is more appropriate when considering Tier account. Our user survey study and its statistical analysis agreed with the arguments made for each of the six comparisons factors.
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Wadly, Fachrid, Arpan, and Muhammad Muttaqin. "IMPLEMENTASI PLATFORM AS A SERVICE (PAAS) PADA DATABASE E-COMMERCE BERBASIS CLOUD COMPUTING." Jurnal Nasional Teknologi Komputer 3, no. 2 (April 30, 2023): 45–58. http://dx.doi.org/10.61306/jnastek.v3i2.88.
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Business competition in the field of information technology continues to increase, one of the technologies itself is cloud computing. Cloud computing can be public or private. One type of cloud computing service used in this study is PaaS. The PaaS cloud computing service provides a platform that users can use to create applications. The Byna Cake shop is a shop that opens a cake business in Aceh, which is located on Pulau Tiga, Aceh, Tamiang. This shop sells various types of cakes which have been produced manually by themselves since 2021. The sales system uses WhatsApp and Instagram media and comes directly to the store, so this system is considered to be less effective and efficient. Therefore, an online sales system is needed, namely by building a web-based E-Commerce application. In implementing this web-based e-commerce application, the author utilizes cloud infrastructure as a cloud provider, namely Microsoft Azure. Computing service used in this research is CMS AbanteCart. Web-based e-commerce application at the Byna Cake store created by configuring a virtual machine to install CMS AbanteCart Packaged By Bitnami on the Microsoft Azure platform. The design used in creating web-based e-commerce applications at the Byna Cake store uses Use Cases and Class Diagrams.
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Qarkaxhija, Jusuf. "Using Cloud Computing as an Infrastructure Case Study- Microsoft Azure." Technium: Romanian Journal of Applied Sciences and Technology 2, no. 3 (May 8, 2020): 93–100. http://dx.doi.org/10.47577/technium.v2i3.473.
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In recent years, the cloud has achieved an immense popularity in the world of tech. This has provided new and improved strategies for cost reduction, and for ensuring better utilisation of cloud resources.
Implementation of this model is continuously increasing in numerous businesses , due to the many benefits that the companies are attaining. These cloud resources can belong to either, the infrastructure or platform model.
A vast attention has been directed towards the virtualization technology, because the cloud is largely relied upon it. With the help of virtualization, one can quickly download apps or websites, from the cloud.
In order to yield the full potential of the cloud, companies should migrate all their current applications to the cloud, and in order to do that- only an internet connection is required.
Migration of the existing systems to a scalable cloud solution, can reduce hardware related costs , such as : servers, installation of operating system, database and licence system costs, deployment of database products , and finally employment of professional staff to develop and maintain the system.
This research attempts to study and analyze Microsoft Azure, in particular the virtual machine - as part of its infrastructure. The main priority lies in establishing a secure cloud data storage system.
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Truong, Linh, Felipe Ayora, Lloyd D’Orsogna, Patricia Martinez, and Dianne De Santis. "Nanopore sequencing data analysis using Microsoft Azure cloud computing service." PLOS ONE 17, no. 12 (December 2, 2022): e0278609. http://dx.doi.org/10.1371/journal.pone.0278609.
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Genetic information provides insights into the exome, genome, epigenetics and structural organisation of the organism. Given the enormous amount of genetic information, scientists are able to perform mammoth tasks to improve the standard of health care such as determining genetic influences on outcome of allogeneic transplantation. Cloud based computing has increasingly become a key choice for many scientists, engineers and institutions as it offers on-demand network access and users can conveniently rent rather than buy all required computing resources. With the positive advancements of cloud computing and nanopore sequencing data output, we were motivated to develop an automated and scalable analysis pipeline utilizing cloud infrastructure in Microsoft Azure to accelerate HLA genotyping service and improve the efficiency of the workflow at lower cost. In this study, we describe (i) the selection process for suitable virtual machine sizes for computing resources to balance between the best performance versus cost effectiveness; (ii) the building of Docker containers to include all tools in the cloud computational environment; (iii) the comparison of HLA genotype concordance between the in-house manual method and the automated cloud-based pipeline to assess data accuracy. In conclusion, the Microsoft Azure cloud based data analysis pipeline was shown to meet all the key imperatives for performance, cost, usability, simplicity and accuracy. Importantly, the pipeline allows for the on-going maintenance and testing of version changes before implementation. This pipeline is suitable for the data analysis from MinION sequencing platform and could be adopted for other data analysis application processes.
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Rajendran, Prem, Sarthak Maloo, Rohan Mitra, Akchunya Chanchal, and Raafat Aburukba. "Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models." Applied Sciences 13, no. 23 (November 22, 2023): 12577. http://dx.doi.org/10.3390/app132312577.
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As cloud computing rises in popularity across diverse industries, the necessity to compare and select the most appropriate cloud provider for specific use cases becomes imperative. This research conducts an in-depth comparative analysis of two prominent cloud platforms, Microsoft Azure and Amazon Web Services (AWS), with a specific focus on their suitability for deploying object-detection algorithms. The analysis covers both quantitative metrics—encompassing upload and download times, throughput, and inference time—and qualitative assessments like cost effectiveness, machine learning resource availability, deployment ease, and service-level agreement (SLA). Through the deployment of the YOLOv8 object-detection model, this study measures these metrics on both platforms, providing empirical evidence for platform evaluation. Furthermore, this research examines general platform availability and information accessibility to highlight differences in qualitative aspects. This paper concludes that Azure excels in download time (average 0.49 s/MB), inference time (average 0.60 s/MB), and throughput (1145.78 MB/s), and AWS excels in upload time (average 1.84 s/MB), cost effectiveness, ease of deployment, a wider ML service catalog, and superior SLA. However, the decision between either platform is based on the importance of their performance based on business-specific requirements. Hence, this paper ends by presenting a comprehensive comparison based on business-specific requirements, aiding stakeholders in making informed decisions when selecting a cloud platform for their machine learning projects.
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Sajid, Rafat Ul Aman, Sirajul Islam, Abul Bashar Khan Rakib, and Amandeep Kaur. "Interpretation on the Google Cloud Platform and Its Wide Cloud Services." International Journal of Security and Privacy in Pervasive Computing 14, no. 1 (January 1, 2022): 1–7. http://dx.doi.org/10.4018/ijsppc.313586.
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Cloud computing is growing wide and first-rate promising technology. There are three forms of cloud computing: infrastructure as a service (IaaS), software as a service (SaaS), and platform as a service (PaaS). There are many cloud service providers. Among these, AWS (Amazon Web Service), Azure (Microsoft Cloud), IBM Cloud, Oracle Cloud, and VMware are immensely popular. In this paper, the authors study the Google Cloud Platform (GCP) and a few principally used popular services. Google Cloud Platform could be a cloud provider; they supply servers and services that are used on-demand and at scale. Google Cloud Platform is the fastest growing cloud service supplier now.
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Bhaskar, Archana, and Rajeev Ranjan. "Optimized memory model for hadoop map reduce framework." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 4396. http://dx.doi.org/10.11591/ijece.v9i5.pp4396-4407.
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Map Reduce is the preferred computing framework used in large data analysis and processing applications. Hadoop is a widely used Map Reduce framework across different community due to its open source nature. Cloud service provider such as Microsoft azure HDInsight offers resources to its customer and only pays for their use. However, the critical challenges of cloud service provider is to meet user task Service level agreement (SLA) requirement (task deadline). Currently, the onus is on client to compute the amount of resource required to run a job on cloud. This work present a novel memory optimization model for Hadoop Map Reduce framework namely MOHMR (Optimized Hadoop Map Reduce) to process data in real-time and utilize system resource efficiently. The MOHMR present accurate model to compute job memory optimization and also present a model to provision the amount of cloud resource required to meet task deadline. The MOHMR first build a profile for each job and computes memory optimization time of job using greedy approach. Experiment are conducted on Microsoft Azure HDInsight cloud platform considering different application such as text computing and bioinformatics application to evaluate performance of MOHMR of over existing model shows significant performance improvement in terms of computation time. Experiment are conducted on Microsoft Azure HDInsight cloud. Overall, good correlation is reported between practical memory optimization values and theoretical memory optimization values.
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Vinutha, D. C., and G. T. Raju. "An Accurate and Efficient Scheduler for Hadoop MapReduce Framework." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 3 (December 1, 2018): 1132. http://dx.doi.org/10.11591/ijeecs.v12.i3.pp1132-1142.
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MapReduce is the preferred computing framework used in large data analysis and processing applications. Hadoop is a widely used MapReduce framework across different community due to its open source nature. Cloud service provider such as Microsoft azure HDInsight offers resources to its customer and only pays for their use. However, the critical challenges of cloud service provider is to meet user task Service level agreement (SLA) requirement (task deadline). Currently, the onus is on client to compute the amount of resource required to run a job on cloud. This work present a novel makespan model for Hadoop MapReduce framework namely OHMR (Optimized Hadoop MapReduce) to process data in real-time and utilize system resource efficiently. The OHMR present accurate model to compute job makespan time and also present a model to provision the amount of cloud resource required to meet task deadline. The OHMR first build a profile for each job and computes makespan time of job using greedy approach. Furthermore, to provision amount of resource required to meet task deadline Lagrange Multipliers technique is applied. Experiment are conducted on Microsoft Azure HDInsight cloud platform considering different application such as text computing and bioinformatics application to evaluate performance of OHMR of over existing model shows significant performance improvement in terms of computation time. Experiment are conducted on Microsoft Azure HDInsight cloud. Overall good correlation is reported between practical makespan values and theoretical makespan values.
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Sfiligoi, Igor, John Graham, and Frank Wuerthwein. "Characterizing network paths in and out of the clouds." EPJ Web of Conferences 245 (2020): 07059. http://dx.doi.org/10.1051/epjconf/202024507059.
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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.
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Туркін, Ігор Борисович, and В'ячеслав Андрійович Лезновскій. "IoT-платформа для вібраційної діагностики промислового обладнання." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 3 (October 5, 2021): 125–39. http://dx.doi.org/10.32620/reks.2021.3.10.
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The subject of study in the article is a digital platform for vibration diagnostics of industrial equipment. The aim is to increase the informativeness of vibration diagnostics processes of industrial equipment by developing and implementing IoT-oriented solutions based on the concept of intelligent sensors and actuators according to the IEEE standard 1451.0-2007. Tasks: to substantiate the feasibility of using platform-oriented technologies for vibration diagnostics of industrial equipment and choose a cloud service for the implementation of the platform, to develop software and hardware solutions for IoT-platform for vibration diagnostics of industrial equipment; calibrate the vibration diagnostic system and check the accuracy of the measurement. The methods used are microservice approach, multilevel architecture, methods for assessing the condition of equipment by vibration indicators. The following results were obtained. The Microsoft Azure IoT platform, which provides the infrastructure for creating and managing cloud applications, was chosen as the cloud computing platform for the industrial equipment vibration diagnostic system. Azure Internet of Things Suite is a Microsoft Azure IoT service designed to integrate and organize data flows, analyze, and present data in a format that helps people make informed decisions. The architecture of the IoT-system of vibration diagnostics of industrial equipment developed and presented in the article is three-level. The level of autonomous sensors provides reading of vibration acceleration indicators and through the digital wireless data transmission channel BLE transmits data to the Hub level, which is implemented based on a single-board microcomputer BeagleBone. The computing power of BeagleBone provides work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the state of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is used. Conclusions. Tests of the IoT system for vibration diagnostics of industrial equipment were performed using a special stand, which allows the calibration of sensors and verification of the accuracy of the measuring system. The correctness of the entire system is confirmed by the coincidence of expected and measured results. The direction of development of the IoT-system for vibration diagnostics of industrial equipment is the development of additional microservices, which will add the possibility of using modern artificial intelligence technologies for complex diagnostics and forecasting of equipment status.
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Kotyk, Vladyslav, and Yevhenii Vavruk. "Comparative Analysis of Server and Serverless Cloud Computing Platforms Vladyslav Kotyk, Yevhenii Vavruk." Advances in Cyber-Physical Systems 7, no. 2 (December 16, 2022): 115–20. http://dx.doi.org/10.23939/acps2022.02.115.
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Cloud computing is emerging as a powerful computing paradigm for the efficient use of resources. However, decisions to move to cloud computing always remain risky from the customer's point of view, considering the benefits they get from it. Existing research on cloud computing is more focused on technical aspects such as security, quality, efficiency, etc. However, research on the implementation of cloud computing is at an early stage. Thus, in this article, an attempt is made to create a model for cost analysis and advantages for deciding on the application of cloud computing. It takes into account various organizational parameters, designing server and serverless architectures using Microsoft Azure Portal cloud platform services and policies of this organization. Also, it makes a comparative characterization of these services according to power and price criteria. A comparative description of these services according to capacity and price criteria is also given. It shows the structure of the test tool for assessment. Evaluation parameters and metrics are defined. In addition, this article contains information about approaches to evaluating cloud platforms according to various criteria that are most important for a developer.
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Reznikov, R. "The Economic Impact of Cloud Technologies on the Industry 4.0 Development." Economic Herald of the Donbas, no. 4 (74) (2023): 67–74. http://dx.doi.org/10.12958/1817-3772-2023-4(74)-67-74.
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This article explores the profound economic impact of cloud technologies on Industry 4.0, focusing on how these technologies are revolutionizing the industrial sector. It delves into the transformation of IT investments from capital expenditure (CAPEX) to operational expenditure (OPEX) due to cloud computing. This shift is making advanced digital technologies more accessible and affordable, particularly for small and medium-sized enterprises (SMEs). By reducing the need for significant upfront investments and providing scalable, pay-as-you-go solutions, cloud computing significantly enhances the return on investment (ROI) for Industry 4.0 initiatives. A comparative analysis of major cloud service providers—Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure—reveals the diverse range of services and economic benefits they offer for Industry 4.0 applications. AWS leads with comprehensive IoT and machine learning services, GCP excels in data analytics and AI capabilities, and Microsoft Azure provides robust enterprise integrations and hybrid cloud solutions. The article also addresses critical challenges such as security, compliance, and the risks associated with cloud provider lock-in. It offers strategic insights into cost management practices that can maximize the economic benefits of cloud adoption, such as leveraging multi-cloud strategies and utilizing auto-scaling and reserved instances. Furthermore, the article includes case studies from leading industrial companies like Siemens, General Electric, BMW, and ABB. These examples illustrate how cloud-based Industry 4.0 solutions enhance operational efficiency, reduce costs, and drive innovation. For instance, Siemens leverages AWS for scalable IoT solutions, GE utilizes Azure for data-driven industrial insights, and BMW employs GCP for advanced data analytics to improve manufacturing processes. In conclusion, cloud technologies are essential enablers of Industry 4.0, offering significant economic advantages and fostering innovation and efficiency. By overcoming historical barriers to entry, especially for SMEs, and providing flexible, scalable solutions, cloud computing is transforming the industrial landscape, driving growth, and facilitating the widespread adoption of advanced manufacturing technologies.
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Levin, Semen M. "Comparative analysis of security models in cloud platforms." Известия ТПУ. Промышленная кибернетика. 2, no. 2 (June 25, 2024): 1–16. http://dx.doi.org/10.18799/29495407/2024/2/50.
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This article is dedicated to the importance of data security on cloud platforms, highlighting the subject relevance in an era where threats to information security are becoming increasingly complex and sophisticated. The primary focus is a comparative analysis of security models employed by leading cloud platforms such as Amazon Web Services, Microsoft Azure, Google Cloud Platform, IBM Cloud, and Oracle Cloud. The research covers various crucial security aspects, including data encryption, identity and access management, monitoring mechanisms and incident response. The article sheds light on current trends and technologies in cloud data protection, including the use of artificial intelligence and machine learning to enhance threat detection efficiency and the implementation of confidential computing and blockchain technologies to improve data protection. Issues of compliance with legal requirements and data security standards are discussed, along with recommendations for organisations to optimise information protection in the cloud environment.
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Ahuja, Sanjay P., and Sindhu Mani. "Empirical Performance Analysis of HPC Benchmarks Across Variations in Cloud Computing." International Journal of Cloud Applications and Computing 3, no. 1 (January 2013): 13–26. http://dx.doi.org/10.4018/ijcac.2013010102.
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High Performance Computing (HPC) applications are scientific applications that require significant CPU capabilities. They are also data-intensive applications requiring large data storage. While many researchers have examined the performance of Amazon’s EC2 platform across some HPC benchmarks, an extensive study and their comparison between Amazon’s EC2 and Microsoft’s Windows Azure is largely missing with metrics such as memory bandwidth, I/O performance, and communication and computational performance. The purpose of this paper is to implement existing benchmarks to evaluate and analyze these metrics for EC2 and Windows Azure that span both Infrastructure-as-a-Service and Platform-as-a-Service types. This was accomplished by running MPI versions of STREAM, Interleaved or Random (IOR) and NAS Parallel (NPB) benchmarks on small and medium instance types. In addition a new EC2 medium instance type (m1.medium) was also included in the analysis. These benchmarks measure the memory bandwidth, I/O performance, communication and computational performance.
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Rezazadeh, Alireza. "A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine-Learning Approach." Forecasting 2, no. 3 (August 6, 2020): 267–83. http://dx.doi.org/10.3390/forecast2030015.
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Predicting the outcome of sales opportunities is a core part of successful business management. Conventionally, undertaking this prediction has relied mostly on subjective human evaluations in the process of sales decision-making. In this paper, we addressed the problem of forecasting the outcome of Business to Business (B2B) sales by proposing a thorough data-driven Machine-Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine-Learning Service (Azure ML). This workflow consists of two pipelines: (1) An ML pipeline to train probabilistic predictive models on the historical sales opportunities data. In this pipeline, data is enriched with an extensive feature enhancement step and then used to train an ensemble of ML classification models in parallel. (2) A prediction pipeline to use the trained ML model and infer the likelihood of winning new sales opportunities along with calculating optimal decision boundaries. The effectiveness of the proposed workflow was evaluated on a real sales dataset of a major global B2B consulting firm. Our results implied that decision-making based on the ML predictions is more accurate and brings a higher monetary value.
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Ahmad Alnaimat, Mohammad, Oleg Kharit, Iryna Mykhailenko, Ihor Palchyk, and Safar Purhani. "Implementation of cloud computing in the digital accounting system of logistics companies." Acta logistica 11, no. 1 (March 31, 2024): 99–109. http://dx.doi.org/10.22306/al.v11i1.461.
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The aim of the article is to determine the capabilities of cloud services for meeting the needs of logistics companies in the management of supply chains and digital accounting. The study provides a review of current academic publications, analysis of official documentation of cloud service provider companies, and expert opinion of the authors of the study. The study was based on information about the 5 most popular cloud services used by companies to perform mathematical calculations. Amazon Web Services focus on reliability and scalability, providing a wide range of data storage and processing services, as well as performing serverless mathematical calculations. Microsoft Azure stands out for its integrated solutions, as well as data management and analytics services. Google Cloud offers a wide range of development and analytics tools, including data visualization and data sharing. Oracle Cloud provides comprehensive financial and database management solutions. SAP Cloud Platform specializes in financial management and analytics solutions. The results of the research open up prospects for studying the problems of integrating services built on different platforms and finding optimal solutions for combining the existing system of mathematical calculations in a logistics company with the offered cloud-based services.
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Vemasani, Preetham, Sai Mahesh Vuppalapati, Suraj Modi, and Sivakumar Ponnusamy. "Achieving Agility through Auto-Scaling: Strategies for Dynamic Resource Allocation in Cloud Computing." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 3169–77. http://dx.doi.org/10.22214/ijraset.2024.60566.
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Abstract: Auto-scaling is a crucial aspect of cloud computing, allowing for the efficient allocation of computational resources in response to immediate demand. This article delves into the concept of auto-scaling, its key components, and the strategies used to effectively manage resources in cloud environments. This study emphasizes the importance of auto-scaling in the cloud computing landscape by exploring its benefits, including cost efficiency, performance optimization, high availability, and scalability [1]. The article explores the various factors to consider when implementing scaling policies, such as selecting the right approach for scaling, whether it be predictive or reactive and the availability of auto-scaling services provided by major cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure [2, 3]. In addition, the paper addresses the challenges and complexities related to configuring auto-scaling systems, cost management, and latency in resource provisioning [4]. The article also showcases case studies that illustrate the successful implementation of auto-scaling in different industries, along with valuable insights and recommended approaches [5]. Lastly, this paper delves into future trends and research directions in auto-scaling techniques, integration with emerging technologies, and potential research areas [6].
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Khot, Aditi Rajan. "A Comparative Analysis of Public Cloud Platforms and Introduction of Multi-Cloud." Volume 5 - 2020, Issue 9 - September 5, no. 9 (September 23, 2020): 448–54. http://dx.doi.org/10.38124/ijisrt20sep234.
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Cloud computing is an accepted widely, emerging paradigm for its ‘pay as you go’ approach, massive economies of scale, and global in minutes concept. Over the years, different cloud providers have emerged with various services to meet the requirements of the end-user. Because of an increase in the diversity of services, the complexity increases. Customers cannot decide the optimal service to fulfill their requirements. This paper provides a comparative analysis of services of top public cloud providers namely, AWS, GCP, Oracle, and Microsoft Azure. Public cloud-provider strives to be efficient in every technological aspect, though some are better for certain tasks than others. This paper, as a solution, introduces the concept of Multi-Cloud computing, to leverage the benefits of the different cloud providers and to maximize their utility in single network architecture.
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Tasnim, Rehnuma, Afrin Akter Mim, Salman Hasan Mim, and Md Ismail Jabiullah. "A Comparative Study on Three Selective Cloud Providers." International Journal on Cybernetics & Informatics 11, no. 4 (August 27, 2022): 167–78. http://dx.doi.org/10.5121/ijci.2022.110413.
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Cloud Computing means a place where we can store our valuable information of data and access the computing and networking services following the pay-as-you-go method without a physical environment. In the present day, cloud computing offers us powerful computing and storage, high availability and security, instant accessibility and adaptation, guaranteed scalability and interoperability, and cost and time effectiveness. Cloud computing has three platforms (IaaS, PaaS, SaaS) with exclusive features which assure to make easy their work for a client, Organization or Trade to build up any kind of IT business. In this paper, we managed a comparison of cloud service features and after the comparison, It's simple to select a certain cloud service from the available features by comparison with three selective cloud providers like Amazon, Microsoft Azure and Digital Ocean. Using the result of this survey to not only find the similarities and differences between various elements of cloud computing but also to propose some topics to look into for further research.
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Tasnim, Rehnuma, Afrin Akter Mim, Salman Hasan Mim, and Md Ismail Jabiullah. "Analysis of the Comparison of Selective Cloud Vendors Services." International Journal on Cloud Computing: Services and Architecture 12, no. 2/3/4/5/6 (December 30, 2022): 01–05. http://dx.doi.org/10.5121/ijccsa.2022.12601.
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Cloud computing refers to a location that allows us to preserve our precious data and use computing and networking services on a pay-as-you-go basis without the need for a physical infrastructure. Cloud computing now provides us with powerful data processing and storage, exceptional availability and security, rapid accessibility and adaption, ensured flexibility and interoperability, and time and cost efficiency. Cloud computing offers three platforms (IaaS, PaaS, and SaaS) with unique capabilities that promise to make it easier for a customer, organization, or trade to establish any type of IT business. We compared a variety of cloud service characteristics in this article, following the comparing, it's straightforward to pick a specific cloud service from the possible options by comparison with three chosen cloud providers such as Amazon, Microsoft Azure, and Digital Ocean. By using findings of this study to not only identify similarities and contrasts across various aspects of cloud computing, as well as to suggest some areas for further study.
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Choudhary, Anurag. "A walkthrough of Amazon Elastic Compute Cloud (Amazon EC2): A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 93–97. http://dx.doi.org/10.22214/ijraset.2021.38764.
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Abstract: Cloud services are being provided by various giant corporations notably Amazon Web Services, Microsoft Azure, Google Cloud Platform, and others. In this scenario, we address the most prominent web service provider, which is Amazon Web Services, which comprises the Elastic Compute Cloud functionality. Amazon offers a comprehensive package of computing solutions to let businesses establish dedicated virtual clouds while maintaining complete configuration control over their working environment. An organization needs to interact with several other technologies; however, instead of installing the technologies, the company may just buy the technology available online as a service. Amazon's Elastic Compute Cloud Web service, delivers highly customizable computing capacity throughout the cloud, allowing developers to establish applications with high scalability. Explicitly put, an Elastic Compute Cloud is a virtual platform that replicates a physical server on which you may host your applications. Instead of acquiring your own hardware and connecting it to a network, Amazon provides you with almost endless virtual machines to deploy your applications while they control the hardware. This review will focus on the quick overview of the Amazon Web Services Elastic Compute Cloud which also containing the features, pricing, and challenges. Finally, unanswered obstacles, and future research directions in Amazon Web Services Elastic Compute Cloud, are addressed. Keywords: Cloud Computing, Cloud Service Provider, Amazon Web Services, Amazon Elastic Compute Cloud, AWS EC2
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Xu, Yifei, Fan Yang-Turner, Denis Volk, and Derrick Crook. "NanoSPC: a scalable, portable, cloud compatible viral nanopore metagenomic data processing pipeline." Nucleic Acids Research 48, W1 (May 22, 2020): W366—W371. http://dx.doi.org/10.1093/nar/gkaa413.
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Abstract Metagenomic sequencing combined with Oxford Nanopore Technology has the potential to become a point-of-care test for infectious disease in public health and clinical settings, providing rapid diagnosis of infection, guiding individual patient management and treatment strategies, and informing infection prevention and control practices. However, publicly available, streamlined, and reproducible pipelines for analyzing Nanopore metagenomic sequencing data are still lacking. Here we introduce NanoSPC, a scalable, portable and cloud compatible pipeline for analyzing Nanopore sequencing data. NanoSPC can identify potentially pathogenic viruses and bacteria simultaneously to provide comprehensive characterization of individual samples. The pipeline can also detect single nucleotide variants and assemble high quality complete consensus genome sequences, permitting high-resolution inference of transmission. We implement NanoSPC using Nextflow manager within Docker images to allow reproducibility and portability of the analysis. Moreover, we deploy NanoSPC to our scalable pathogen pipeline platform, enabling elastic computing for high throughput Nanopore data on HPC cluster as well as multiple cloud platforms, such as Google Cloud, Amazon Elastic Computing Cloud, Microsoft Azure and OpenStack. Users could either access our web interface (https://nanospc.mmmoxford.uk) to run cloud-based analysis, monitor process, and visualize results, as well as download Docker images and run command line to analyse data locally.
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Al-Ozeer, Ali ZA, Alaa M. Al-Abadi, Tariq Abed Hussain, Alan E. Fryar, Biswajeet Pradhan, Abdullah Alamri, and Khairul Nizam Abdul Maulud. "Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq." Water 13, no. 23 (November 24, 2021): 3330. http://dx.doi.org/10.3390/w13233330.
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Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms—namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Averaged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM)—were run on the Microsoft Azure cloud computing platform to model the groundwater potential. We investigated the relationship between 512 operating boreholes with a specified specific capacity and 14 groundwater-influencing occurrence factors. The unconfined aquifer in the Nineveh plain, Mosul Governorate, northern Iraq, was used as a case study. The groundwater-influencing factors used included elevation, slope, curvature, topographic wetness index, stream power index, soil, land use/land cover (LULC), geology, drainage density, aquifer saturated thickness, aquifer hydraulic conductivity, aquifer specific yield, depth to groundwater, distance to faults, and fault density. Analysis of the contribution of these factors in groundwater potential using information gain ratio indicated that aquifer saturated thickness, rainfall, hydraulic conductivity, depth to groundwater, specific yield, and elevation were the most important factors (average merit > 0.1), followed by geology, fault density, drainage density, soil, LULC, and distance to faults (average merit < 0.1). The average merits for the remaining factors were zero, and thus, these factors were removed from the analysis. When the selected ML classifiers were used to estimate groundwater potential in the Azure cloud computing environment, the DJ and BDT models performed the best in terms of all statistical error measures used (accuracy, precision, recall, F-score, and area under the receiver operating characteristics curve), followed by DF and LD-SVM. The probability of groundwater potential from these algorithms was mapped and visualized into five groundwater potential zones: very low, low, moderate, high, and very high, which correspond to the northern (very low to low), southern (moderate), and middle (high to very high) portions of the study area. Using a cloud computing service provides an improved platform for quickly and cheaply running and testing different algorithms for predicting groundwater potential.
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Mansour, Faiza A. M., and Najat A. Atbaiga. "A Brief Comparison of Google Cloud and iCloud Services." Al-Mukhtar Journal of Basic Sciences 22, no. 1 (April 30, 2024): 92–102. http://dx.doi.org/10.54172/7vcp1e92.
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The provision of computing resources on demand via the Web with pay-per-use billing is known as cloud computing. Small organizations find cloud computing a very competitive and attractive solution due to its pay-per-use model. Cloud computing delivers many services through the Internet. Services include databases, platforms, data storage, and networking resources hosted at remote data centers. The scalability, flexibility, and cost-effectiveness of cloud computing contributed to its rising popularity. There are several significant players in the cloud computing market, including Google Cloud, Amazon Web Services, Microsoft Azure, etc., each offering a wide range of services and advantages. Considering various factors such as security, cost, reliability, and functionality, choosing the best option presented among different cloud service providers is a challenging task. This paper introduces an overview of cloud computing, its architecture, characteristics, and briefly describes Google Cloud and iCloud services. The main objective of this work is to compare the services provided by the selected cloud platforms. The findings indicated several robust services offered by both Google Cloud and iCloud. The user’s requirements are the basis for the selection process.
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Elfaki, Abdelrahman Osman, Mohammed Abduljabbar, Luqman Ali, Fady Alnajjar, Dua’a Mehiar, Ashraf M. Marei, Tareq Alhmiedat, and Adel Al-Jumaily. "Revolutionizing Social Robotics: A Cloud-Based Framework for Enhancing the Intelligence and Autonomy of Social Robots." Robotics 12, no. 2 (March 24, 2023): 48. http://dx.doi.org/10.3390/robotics12020048.
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Social robots have the potential to revolutionize the way we interact with technology, providing a wide range of services and applications in various domains, such as healthcare, education, and entertainment. However, most existing social robotics platforms are operated based on embedded computers, which limits the robot’s capabilities to access advanced AI-based platforms available online and which are required for sophisticated physical human–robot interactions (such as Google Cloud AI, Microsoft Azure Machine Learning, IBM Watson, ChatGPT, etc.). In this research project, we introduce a cloud-based framework that utilizes the benefits of cloud computing and clustering to enhance the capabilities of social robots and overcome the limitations of current embedded platforms. The proposed framework was tested in different robots to assess the general feasibility of the solution, including a customized robot, “BuSaif”, and commercialized robots, “Husky”, “NAO”, and “Pepper”. Our findings suggest that the implementation of the proposed platform will result in more intelligent and autonomous social robots that can be utilized by a broader range of users, including those with less expertise. The present study introduces a novel methodology for augmenting the functionality of social robots, concurrently simplifying their utilization for non-experts. This approach has the potential to open up novel possibilities within the domain of social robotics.
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Rahman, M. Azizur, Md Shihab Shakur, Md Sharjil Ahamed, Shazid Hasan, Asif Adnan Rashid, Md Ariful Islam, Md Sabit Shahriar Haque, and Afzaal Ahmed. "A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing." Automation 3, no. 3 (August 1, 2022): 400–425. http://dx.doi.org/10.3390/automation3030021.
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With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes.
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Fams, Retnawati Siregar, and Arnida Wahyuni Lubis. "Konsep Penguat Usaha Ekonomi Rakyat Dengan Menggunakan Cloud Computing Literature Study." Al-Kharaj : Jurnal Ekonomi, Keuangan & Bisnis Syariah 6, no. 2 (February 12, 2023): 685–97. http://dx.doi.org/10.47467/alkharaj.v6i2.3227.
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Dengan adanya era digital saat ini maka clout computing sangat familiar di kalangan perusahaan. Istilah yang sering kita dengar adalah komputasi awan yang terkenal dengan bagian data yang disimpan secara virtual kapan dan dimanapun pemakai dapat menyimpanan dan mengaksesa data . Dari data yang dirilis bagian rekomendasi ditahun 2022 adanya 5 rekomendasi perusahaan penyedia layanan cloud computing, misalnya Microsoft Azure, Google Cloud, Alibaba Cloud, Google Cloud Platform, Oracle Cloud Infrastructure, Amazon Web Services. Seperti database, dalam jaringan internet layanan penyimpanan akan menyediakan sumber daya yang lebih inovatif, cepat, dan fleksibel kepada para penggunanya. Dari yang dirujuk dengan Undang-undang (UU) No. 28 Tahun 2008 ,usaha menengah bagian dari ekonomi produktif yang berdiri sendiri dilakukan perorangan atau badan usaha terdiri dari perorangan atau badan usaha yang bukan anak perusahaan atau cabang perusahaan yang dimiliki. Metode penelitian yang penulis digunakan dalam penulisan jurnal ini adalah dengan menggunakan metode penelitian kualitatif yang dilakukan dengan studi literatur, dari jurnal , sumber lain ataupun buku data akan dikumpulkan. Dalam kegiatan bisnis adanya manfaat cloud computing dengan Pendekatan metode ini dapat memberikan pengetahuan terkait dengan fitur-fitur. Hasil dari analisis peper ini adalah Banyak pelaku sektor usaha ekonomi rakyat yang belum menerapkan teknologi informasi ke dalam proses bisnisnya yang dinyatakan dengan surve, Menset pelaku sektor usaha ekonomi rakyat dengan menggunakan cloud computing menggunakan biaya yang mahal , Pemahaman pelaku sektor usaha ekonomi rakyat dalam perangkat server sulit dalam pengoperasiannya.
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Mughele, Ese Sophia, Sunday Ovie Okuyade, and Ifeanyi Mirian Oyem. "A REVIEW OF THE IMPACT OF COVID-19 ON SERVERLESS COMPUTING TECHNOLOGY." FUDMA JOURNAL OF SCIENCES 8, no. 3 (June 30, 2024): 111–18. http://dx.doi.org/10.33003/fjs-2024-0803-2478.
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The Covid-19 pandemic had a profound effect on technology in general, and serverless computing is no exception. Covid-19 pandemic has shaped the field of serverless computing, and how technology has evolved in response. Serverless computing technology have been adapted to meet the need for remote working, and how the technology has changed in terms of scalability and cost-effectiveness. This pandemic has affected virtually every aspect of daily life as significant measures are being taken to limit the spread of the virus. The pandemic has changed not only the way companies operate, but also the way they have been able to survive. Studies indicate increased requests for cloud services ranging from resident users, particularly for telecommuting, entertainment, commerce, to education, and as a result, causing traffic shifts at the core of the Internet. Covid-19 had such a significant impact on cloud services that there is an unprecedented amount of demand for cloud service providers like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. This study used data from a variety of sources to analyse the impact of serverless computing during the pandemic and to justify its significance for a pandemic-affected business. It also reviewed the pre-Covid, Covid and post-Covid-19 era. Two survey reports were used in this study and the effect of Covid-19 on Serverless computing. This paper emphasizes the benefits and adoption of Serverless computing during the pandemic, in contrast to other studies that concentrated on the impact of the Covid-19 epidemic on the cloud computing environment.
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Kelly, Christopher, Nikolaos Pitropakis, Alexios Mylonas, Sean McKeown, and William J. Buchanan. "A Comparative Analysis of Honeypots on Different Cloud Platforms." Sensors 21, no. 7 (April 1, 2021): 2433. http://dx.doi.org/10.3390/s21072433.
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In 2019, the majority of companies used at least one cloud computing service and it is expected that by the end of 2021, cloud data centres will process 94% of workloads. The financial and operational advantages of moving IT infrastructure to specialised cloud providers are clearly compelling. However, with such volumes of private and personal data being stored in cloud computing infrastructures, security concerns have risen. Motivated to monitor and analyze adversarial activities, we deploy multiple honeypots on the popular cloud providers, namely Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure, and operate them in multiple regions. Logs were collected over a period of three weeks in May 2020 and then comparatively analysed, evaluated and visualised. Our work revealed heterogeneous attackers’ activity on each cloud provider, both when one considers the volume and origin of attacks, as well as the targeted services and vulnerabilities. Our results highlight the attempt of threat actors to abuse popular services, which were widely used during the COVID-19 pandemic for remote working, such as remote desktop sharing. Furthermore, the attacks seem to exit not only from countries that are commonly found to be the source of attacks, such as China, Russia and the United States, but also from uncommon ones such as Vietnam, India and Venezuela. Our results provide insights on the adversarial activity during our experiments, which can be used to inform the Situational Awareness operations of an organisation.
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Bebortta, Sujit, Saneev Kumar Das, Meenakshi Kandpal, Rabindra Kumar Barik, and Harishchandra Dubey. "Geospatial Serverless Computing: Architectures, Tools and Future Directions." ISPRS International Journal of Geo-Information 9, no. 5 (May 7, 2020): 311. http://dx.doi.org/10.3390/ijgi9050311.
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Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data.
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Somasundaram, Prakash, and K. Aishwarya Pillai. "Optimizing Trial Experiences in Cloud Platforms: Challenges, Strategies, and Impact on User Engagement and Conversion Rate." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 5 (January 30, 2024): 34–38. http://dx.doi.org/10.35940/ijrte.e7991.12050124.
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This article delves into the significance of trial experiences in the context of cloud-based solutions, a crucial aspect of today's digital business landscape. As cloud platforms reshape service delivery, the Freemium and Free Trial models emerge as compelling strategies for customer engagement. These models not only offer a glimpse into the service's capabilities but also serve as a critical touchpoint for building trust and rapport with potential customers. Optimizing trial experiences, however, comes with a set of challenges, including balancing feature accessibility with the need to incentivize paid conversions and tailoring the trial to diverse user needs and expectations. This article extensively examines the optimization of trial experiences within cloud platforms, encompassing hurdles, strategic approaches, and their profound influence on user engagement and conversion rates. It highlights the delicate art of designing trial experiences that are sufficiently feature-rich to demonstrate value yet limited enough to encourage upgrade to paid versions. The article also discusses how personalization and customer feedback can be leveraged to enhance trial experiences. By analyzing key industry players like Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP), the article sheds light on how these frontrunners utilize trial experiences to captivate audiences and fortify their customer base. It explores their distinct approaches in offering trials, the impact on market positioning, and how they balance the need for security and compliance with user accessibility. The insights from these industry giants provide valuable lessons for other players in the cloud computing sphere looking to harness the power of trial experiences for customer acquisition and retention.
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Graham, Shawn, Damien Huffer, and Jeff Blackadar. "Towards a Digital Sensorial Archaeology as an Experiment in Distant Viewing of the Trade in Human Remains on Instagram." Heritage 3, no. 2 (April 13, 2020): 208–27. http://dx.doi.org/10.3390/heritage3020013.
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It is possible to purchase human remains via Instagram. We present an experiment using computer vision and automated annotation of over ten thousand photographs from Instagram, connected with the buying and selling of human remains, in order to develop a distant view of the sensory affect of these photos: What macroscopic patterns exist, and how do these relate to the self-presentation of these individual vendors? Using Microsoft’s Azure cloud computing and machine learning services, we annotate and then visualize the co-occurrence of tags as a series of networks, giving us that macroscopic view. Vendors are clearly trying to mimic ‘museum’-like experiences, with differing degrees of effectiveness. This approach may therefore be useful for even larger-scale investigations of this trade beyond this single social media platform.
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KIPORENKO, Svitlana. "FEATURES OF USING CLOUD TECHNOLOGIES IN EDUCATION." "EСONOMY. FINANСES. MANAGEMENT: Topical issues of science and practical activity", no. 4 (44) (April 2019): 181–89. http://dx.doi.org/10.37128/2411-4413-2019-4-19.
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The article investigates cloud technologies and features of their use in educational institutions. The essence of cloud technologies in education is determined. It is noted that the modern institution of higher education provides usage of information and communication technologies, and in particular, cloud technologies. The main characteristics (features) which characterize cloud services are described here. It is noted that usage of cloud services has benefits and problems as well. There are such mandatory characteristics of cloud computing as self-service on demand, universal access to the network, resource pooling, elasticity, consumption accounting. Four models of deployment of cloud technologies are revealed in the article. They are corporate, public, group and hybrid. It is admitted that the most appropriate model of deployment of cloud technologies in the infrastructure of higher education institutions is hybrid. Cloud technologies support such activities as: communication, collaboration and co-operation, which determines certain areas of their usage. The architecture of cloud technologies is presented in three levels: software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS). The most popular cloud services which are used in various spheres, including educational institutions, are analyzed here. Office 365, Microsoft Planner, Microsoft SkyDrive, Google Drive, Google Talk, Google Docs, these are the common cloud services which are selected in SaaS. Popular software platforms which are presented in the PaaS segment are Windows Azure, Google App Engine, Cloud9 IDE, Ideone IDE. Each of the cloud platforms that is described in the article has its own peculiarities. Possible areas of application of cloud technologies in the educational process are identified. They are free access to the programs and services used in the educational process, planning activities, implementation of the working regime of teachers and employees, the organization and conduct of scientific online conferences, seminars, forums, round tables, trainings, joint project work of students, individual independent work of students, electronic interaction with entrants.
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Trippner, Paweł, and Rafał Jóźwicki. "Using Machine Learning for Short-term Capital Investment in the Polish Stock Market." Journal of Intercultural Management 15, no. 4 (December 1, 2023): 93–104. http://dx.doi.org/10.2478/joim-2023-0019.
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Abstract Objective The main purpose of this article is to assess the potential implications of machine learning in making investment decisions when investing capital in stock markets. The analysis carried out focuses on the so-called day-trading, i.e., investing for very short periods of time, covering only one stock market session. The hypothesis adopted by the authors is that the use of machine learning can, under certain conditions, effectively contribute to attractive rates of return for players making short-term investments. Methodology The study used Microsoft Machine Learning Studio’s Azure tool to enable machine learning-based computing. Thanks to this publicly available computing platform, any potential interested investor can create a model and test it. An important assumption of the described study is the adoption of a short investment horizon for the calculation. The calculations used data from five stock market sessions, so that the most recent data is taken into account. Findings Based on the calculations, the authors observed that the methodology adopted for applying machine learning to investment decision-making can be a valuable tool to help make short-term investment decisions. Value Added The research made can be used in a practical way by investors when they transact in the stock market. Recommendations It should be noted that the presented method requires updating the data on which the predictions are made every time. Further in-depth research is also needed to determine the impact of the number of financial instruments on the effectiveness of the learning process.
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Synkevych, R. O., S. Ya Maistrenko, T. O. Dontsov-Zahreba, O. V. Khalchenkov, O. O. Polonskyi, and O. I. Udovenko. "Ensuring the functioning of the system for forecasting air pollution in the cloud infrastructure by automating the creation of virtual machines." Mathematical machines and systems 2 (2022): 68–76. http://dx.doi.org/10.34121/1028-9763-2022-2-68-76.
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The paper improves the web service Povitrya aimed at forecasting air pollution in case of man-made disasters by saving the configured CALPUFF atmospheric transport model in a pre-configured image and integrating into the system of software tools for the automatic creation of virtual machines from a pre-saved image after the user request. To do this, an image based on the Ubuntu operating system was configured in the Ukrainian National Grid Infrastructure, to-gether with the necessary CALPUFF atmospheric transport model libraries. Based on the saved image, one can quickly create and run a virtual machine and start the calculations. After the cal-culation, the Povitrya system removes the virtual machine, freeing up cloud resources for other tasks. The example of test calculations shows that due to the transfer of the module of calcula-tion of atmospheric transport to the cloud infrastructure it was possible to reduce the calculation time of the CALPUFF model by almost 2 times, and the download time of weather forecast data was reduced by more than 10 times. In total, the system calculation time after the user's request was reduced by 4 times for the test configuration. In addition, the resilience of the system to cloud infrastructure failures has been increased. The created archive with the operating system and configured for the CALPUFF model runs can also be transferred to other private clouds (for example, Amazon, Google Cloud Platform, Microsoft Azure) and their virtual machines could be used. Thus, the developed web system corresponds to modern trends in the implementation of cloud computing technologies, if necessary, allows scaling and can be adapted to other pri-vate or public cloud computing infrastructures. The system is available for registered users by the link: http://cloud-2.bitp.kiev.ua/ airsystem_english/airsystem.html.
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Iñiguez, Carlos, and Julio Sandobalín. "Web User Interface Design of a Visual Editor for Cloud Infrastructure Modeling." Revista Politécnica 52, no. 1 (August 1, 2023): 83–94. http://dx.doi.org/10.33333/rp.vol52n1.09.
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Cloud computing has evolved the way IT technicians manage infrastructure resources to support software applications. Leasing equipment and services from cloud infrastructure providers, such as Amazon Web Services, Microsoft Azure, etc., has replaced the traditional strategy of locally installing expensive equipment. Nowadays, IT technicians model the infrastructure they need by writing scripts, then running these scripts in the provider web platform. However, writing scripts becomes a complex task that involves interacting with the Command Line Interface and knowing the commands each provider imposes. In this context, replacing textual with visual interaction becomes a need. The purpose is to fesign the user interface (UI) of a visual web editor to model the cloud infrastructure resources. The User-Centered Design (UCD) methodology was applied to design the UI. As part of DCU, a study of the UIs of diagramming online tools was conducted to identify UI design patterns; those that expert designers use when designing visual editors.The results show that a set of 11 UI patterns for designing visual editor UIs was defined. In addition, a pattern language was built considering the relationships between patterns. By using the pattern language, the visual editor UI design was composed. As conclusions, the pattern language provided a logical way to compose the visual editor UI. In this sense, the proposed UI together with the pattern language can become a reference point for designing UIs in this domain. In future work, the effectiveness of the UI in reducing the complexity of defining the cloud infrastructure will be evaluated with user tests.
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Kishiyama, Brian, and Izzat Alsmadi. "A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption." International Journal of Science, Technology and Society 12, no. 2 (March 20, 2024): 81–87. http://dx.doi.org/10.11648/j.ijsts.20241202.11.
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Cloud Service Providers, exemplified by industry leaders like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, deliver a dynamic array of cloud services in an ever-evolving landscape. This sector is witnessing substantial growth, with enterprises such as Netflix and PayPal heavily relying on cloud infrastructure for various needs such as data storage, computational resources, and various other services. The adoption of cloud solutions by businesses not only facilitates cost reduction but also fosters flexibility and supports scalability. Despite the undeniable advantages, concerns surrounding security and privacy persist in the realm of Cloud Computing. Given that Cloud services are accessible via the internet, there is a potential vulnerability to unauthorized access by hackers or malicious entities from anywhere in the world. A crucial aspect of addressing this challenge is the implementation of robust security measures, particularly focusing on data protection. To safeguard data in the Cloud, a fundamental recommendation is the encryption of data prior to uploading. Encryption should be maintained consistently, both during storage and in transit. While encryption enhances security, it introduces a potential challenge for data owners who may need to perform various operations on their encrypted data, such as accessing, modifying, updating, deleting, reading, searching, or sharing them with others. One viable solution to balance the need for data security and operational functionality is the adoption of Searchable Encryption (SE). SE operates on encrypted data, allowing authorized users to perform certain operations without compromising the security of sensitive information. The effectiveness of SE has notably advanced since its inception, and ongoing research endeavors aim to further enhance its capabilities. This paper provides a comprehensive review of the functionality of Searchable Encryption, with a primary focus on its applications in Cloud services during the period spanning 2019 to 2023. Additionally, the study evaluates one of its prominent schemes, namely Fully Homomorphic Encryption (FHE). The analysis indicates an overall positive trajectory in SE research, showcasing increased efficiency as multiple functionalities are aggregated and rigorously tested.
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Petrenko, Alexei, and Sergei Petrenko. "Quantum Resilience Estimation Method Blockchain." Voprosy kiberbezopasnosti, no. 3(49) (2022): 2–22. http://dx.doi.org/10.21681/2311-3456-2022-3-2-22.
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Abstract Purpose of work is the development of a new method for estimating the quantum resilience of modern blockchain platforms based on the effective solution of cryptanalysis problems for asymmetric encryption schemes (RSA, El-Gamal) and digital signature (DSA, ECDSA or RSA-PSS), based on computationally difficult problems of factorization and discrete logarithm. Research method is the use of quantum algorithms providing exponential gain (eg Shor’s algorithm) and quadratic gain (eg Grover’s algorithm). Due to the fact that the class of problems solved by quantum algorithms in polynomial time cannot yet be significantly expanded, more attention is paid to cryptanalysis based on the quantum Shor algorithm and other polynomial algorithms. Results of the study include a classification of well-known algorithms and software packages for cryptanalysis of asymmetric encryption schemes (RSA, El-Gamal) and digital signature (DSA, ECDSA or RSA-PSS) based on computationally difficult problems of factorization and discrete logarithm has been built. A promising method for solving problems of cryptanalysis of asymmetric encryption schemes (RSA, ElGamal) and digital signature (DSA, ECDSA or RSA-PSS) of known blockchain platforms in polynomial time in a quantum computing model is proposed. Algorithms for solving problems of quantum cryptanalysis of two-key cryptography schemes of known blockchain platforms in polynomial time are developed, taking into account the security of the discrete algorithm (DLP) and the discrete elliptic curve algorithm (ECDLP). A structural and functional diagram of the software package for quantum cryptanalysis of modern blockchain platforms “Kvant-K”, adapted to work in a hybrid computing environment of the IBM Q quantum computer (20 and 100 qubits) and the IBM BladeCenter (2022) supercomputer, has been designed. A methodology has been developed for using the “Kvant-K” software package to assess the quantum stability of blockchain platforms: InnoChain (Innopolis University), Waves Enterprise (Waves, Vostok), Hyperledger Fabric (Linux, IBM), Corda Enterprise, Bitfury Exonum, Blockchain Industrial Alliance, Exonum (Bitfury CIS), NodesPlus (b41), Masterchain (Sberbank), Microsoft Azure Blockchain, Enterprise Ethereum Alliance, etc. Practical relevance: The developed new solution for computationally difficult problems of factorization and discrete logarithm, given over finite commutative (and non-commutative) associative algebras, in a quantum model of computing in polynomial time. It is essential that the obtained scientific results formed the basis for the development of the corresponding software and hardware complex “Kvant-K”, which was tested in a hybrid computing environment (quantum computer IBM Q (20 and 100 qubits) and/or 5th generation supercomputer: IBM BladeCenter (2022), RCS based on FPGA Virtex UltraScale (2020), RFNC-VNIIEF (2022) and SKIF P-0.5 (2021). An appropriate method for estimating the quantum stability of these blockchain platforms based on the author’s models, methods and algorithms of quantum cryptanalysis has been developed and tested. Keywords: blockchain and distributed ledger technologies (DLT), SMART contracts, blockchain security threat model, quantum security threat, cryptographic attacks, quantum cryptanalysis, quantum and post-quantum cryptography, quantum algorithms Shor, Grover and Simon algorithms, quantum Fourier transform, factorization and discrete logarithm problem, post-quantum cryptography, quantum resilience of blockchain platforms.
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Sahu, Kirti Sundar, Joel A. Dubin, Shannon E. Majowicz, Sam Liu, and Plinio P. Morita. "Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights." JMIR Public Health and Surveillance 10 (March 20, 2024): e46903. http://dx.doi.org/10.2196/46903.
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Background The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google’s GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. Objective This study investigates in-home mobility data from ecobee’s smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google’s residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. Methods Motion sensor data were acquired from the ecobee “Donate Your Data” initiative via Google’s BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces—Ontario, Quebec, Alberta, and British Columbia—during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. Results The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google’s data set. Examination of Google’s daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. Conclusions This study’s findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google’s out-of-house residential mobility data and ecobee’s in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.
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Jóźwicki, Rafał. "Forecasting Prices of Shares Listed on the Warsaw Stock Exchange Using Machine Learning." Journal of Intercultural Management 14, no. 3 (September 1, 2022): 63–78. http://dx.doi.org/10.2478/joim-2022-0012.
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Abstract Objective: The technology developing before our eyes is entering many areas of life and has an increasing influence on shaping human behavior. Undoubtedly, it can be stated that one such area is trading on stock exchanges and other markets that offer investors the opportunity to allocate their capital. Thanks to widespread access to the Internet and the computing capabilities of computers used in the daily activities of investors, the nature of their working has changed significantly, compared to what we observed even 10–15 years ago. At present, stock exchange orders may be placed in person using various types of brokerage investment accounts, which allow the investor to view real-time quotations which opens up a whole new range of opportunities for investorsIts skillful application during the stock market game can positively influence a player’s investment performance.Machine learning is a branch of artificial intelligence and computer science that focuses on using data and algorithms to solve decision-making problems based on large amounts of information. In machine learning, algorithms find patterns and relationships in large data sets and make the best decisions and predictions based on this analysis. Methodology: The main objective of this paper is to investigate and evaluate the applicability of machine learning for investment decisions in equity markets. The analysis undertaken focuses on so-called day-trading, i.e. investing for very short periods of time, often involving only a single trading session. The hypothesis adopted is that the use of machine learning can contribute to a positive return for a stock market player making short-term investments. Findings: This paper uses the Azure Microsoft Machine Learning Studio tool to enable machine learning-based calculations. It is a widely available cloud computing platform that provides an investor interested in creating a model and testing it. The calculations were made according to two schemes. The first involves teaching the model by taking 50% of the companies randomly selected from all companies, while the second involves teaching the model by taking 80% of the companies randomly selected from all companies. Value Added: The results from the study indicate that investors can use machine learning to earn returns that are attractive to them. Depending on the teaching model (50% or 80% companies), daily returns can range from 1.07% to even 4.23%. Recommendations: The results obtained offer investors the prospect of using the method presented in the article in their capital management strategies, which of course requires them to adapt the techniques used so far to the specifics of machine learning. However, it is necessary to note that the presented method requires that each time the data on which the forecast was made be updated..Further research is needed to determine the impact of the number of companies on the effectiveness of the learning process.
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Reheta, D. M. "Windows Azure cloud platform." CTE Workshop Proceedings 1 (March 21, 2013): 111. http://dx.doi.org/10.55056/cte.153.
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The functions and design of adaptive information-computing networks concentrates on the concept of electronic data processing based on cloud computing information technology using the Windows Azure platform. This platform is based on running a virtual machine for each instance of an interactive application. According to this concept, networked virtual objects are formed thanks to a special user interface supported by the system programs by means of network configuration tools. These are network virtual sites as a situational component of a logical network infrastructure. Windows Azure sells two cloud models in full: Platform as a Service (PaaS) and Infrastructure as a Service (IaaS).
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S, Sivakumar, Rajasekaran Kondareddy, and Kalyani Ayyemperumal. "Building SaaS solutions using microsoft azure for achieving safe and secure tax related software." Scientific Temper 14, no. 02 (June 6, 2023): 521–26. http://dx.doi.org/10.58414/scientifictemper.2023.14.2.45.
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Several services offered by Microsoft Azure Cloud Platform are used for building, running and managing applications. These Services include Azure Active Directory (AD), Azure Virtual Machines (VMs), Azure App Services, Azure DevOps etc have been used to develop this system which can manage all real estate tax servicing needs. It significantly reduces servicing costs with the Tax Outsourcing platform. This system is a SaaS product that many customers request to maintain current tax status on loan portfolios to ensure taxes are paid on time. The contracted loans include tax identification numbers and parcel numbers, current year taxes, back taxes that are past due, information about tax redemption, etc.
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Muhammad Fahmi, I Ketut Gede Suhartana, and I Komang Ari Mogi. "PERANCANGAN EXAM TRAINING SEBAGAI SISTEM PEMBELAJARAN WEB DENGAN MICROSOFT AZURE UNTUK PESERTA UJIAN SERTIFIKASI." Jurnal Pengabdian Informatika 1, no. 1 (November 1, 2022): 253–58. http://dx.doi.org/10.24843/jupita.2022.v01.i01.p36.
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Exam Training dibangun berbasis website untuk mengatasi masalah peserta pada cloud fundamental track di Microsoft Indonesia dalam menghadapi ujian sertifikasi. Perancangan dan Pengembangan Exam Training menggunakan platform moodle sebagai frontend nya dan microsoft azure pada sisi backend nya. Penerapan microsoft azure dalam proses perancangan sistem memberikan kemudahan kepada developer dikarenakan tidak perlu memikirkan kebutuhan infrastruktur secara on-premises. Pengujian sistem menggunakan blackbox testing untuk menguji fungsionalitas sistem. Dari proses pengujian tersebut diperoleh masukan dan keluaran yang diharapkan.
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Shanahan, Hugh P., Anne M. Owen, and Andrew P. Harrison. "Bioinformatics on the Cloud Computing Platform Azure." PLoS ONE 9, no. 7 (July 22, 2014): e102642. http://dx.doi.org/10.1371/journal.pone.0102642.
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Muslim, Much Aziz, and Nur Astri Retno. "Implementasi Cloud Computing Menggunakan Metode Pengembangan Sistem Agile." Scientific Journal of Informatics 1, no. 1 (May 31, 2015): 29–37. http://dx.doi.org/10.15294/sji.v1i1.3639.
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Cloud computing merupakan sebuah teknologi yang menyediakan layanan terhadap sumber daya komputasi melalui sebuah jaringan. Sumber daya yang di sediakan di dalam cloud computing meliputi mesin, media penyimpanan data, sistem operasi dan program aplikasi. Fitur dari cloud computing dipercaya akan jauh lebih hemat dan memuaskan. Masalah yang muncul adalah bagaimana mengimplementasi Cloud Computing dengan menggunakan Windows Azure Pack dan bagaimana provisioning Windows Azure Pack SQL Database. Fokus pada penelitian ini adalah pada proses deploying dan provisioning SQL Database Server. Pengimplementasian cloud computing menggunakan metode pengembangan sistem agile dengan langkah-langkah meliputi perencanaan, implementasi, pengujian (test), dokumentasi, deployment dan pemeliharaan. Untuk menjalankan proses tersebut kebutuhan perangkat yang dipersiapkan meliputi perangkat keras seperti PC Server Cisco UCS C240 M3S2, Hardisk 8753 GB, 256 GB RAM, bandwith minimal 1 Mbps dan kebutuhan perangkat lunak meliputi Windows Server 2012 R2, VMM, Windows Azure Pack, IIS, SQL Server 2012 dan Web Patform Installer. Hasil dari implementasi cloud computing menggunakan metode pengembangan sistem agile adalah terbentuknya sebuah sistem cloud hosting provider dengan menggunakan Windows Azure Pack dan SQL Server 2012 sebagai sistem utama dan pengelolaan database menggunakan Microsoft SQL Server Management
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Harfoushi, Osama, Dana Hasan, and Ruba Obiedat. "Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison." Modern Applied Science 12, no. 7 (June 21, 2018): 49. http://dx.doi.org/10.5539/mas.v12n7p49.
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The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals’ opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.
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Abdelghany, Ahmed, Mohamed Gamal, Dr Tarek Aly, and Prof Dr Mervat Gheith. "Governance for Azure and AWS Cloud Services." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 3185–97. http://dx.doi.org/10.22214/ijraset.2024.60595.
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Abstract: These days, cloud computing services are utilized in all business, academic, and governmental domains to build IT infrastructure. Cloud computing services are one IT solution that is becoming more and more popular among different types of enterprises. Cloud computing services face many challenges, like how to protect and raise user knowledge. These problems and difficulties also pertain to cloud computing services, which could result in a number of important risk areas. One of the problems is not being aware of the hazards associated with cloud computing services and the controls that are required to reduce these risks. Because governance is difficult to execute in the cloud computing environment and has the responsibility of assessing performance and adherence to predetermined goals and objectives, it hinders the adoption of cloud computing services. This study aims to develop a model that ensures that the required controls are applied in cloud services offered by Microsoft Azure and Amazon Web Services (AWS) and assesses the risk if the controls are not applied.