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Journal articles on the topic 'Scalable Cloud Architecture'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Dr. Pradeep Laxkar and Dr. Nilesh Jain. "A Review of Scalable Machine Learning Architectures in Cloud Environments: Challenges and Innovations." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2907–16. https://doi.org/10.32628/cseit25112764.

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As the demand for machine learning (ML) and data analysis grows across industries, the need for scalable and efficient cloud-based architectures becomes critical. The increase in of data generation, along with the increasing demand for advanced analytics and machine learning (ML), has make necessary the development of scalable architectures in cloud environments. Cloud computing provides a flexible and scalable solution, allowing organizations to efficiently process large datasets and deploy complex ML models without traditional hardware limitations. The review paper explores the various cloud

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Journal, of Global Research in Electronics and Communications. "A Review of Scalable Machine Learning Architectures in Cloud Environments: Challenges and Innovations." Journal of Global Research in Electronics and Communications 1, no. 4 (2025): 7–11. https://doi.org/10.5281/zenodo.15115138.

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As the demand for machine learning (ML) and data analysis grows across industries, the need for scalable and efficient cloud-based architectures becomes critical. The increase in of data generation, along with the increasing demand for advanced analytics and machine learning (ML), has make necessary the development of scalable architectures in cloud environments. Cloud computing provides a flexible and scalable solution, allowing organizations to efficiently process large datasets and deploy complex ML models without traditional hardware limitations. The review paper explores the various cloud

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Santhosh, Podduturi. "Architectural Patterns for ML in Microservices & Cloud Architecture." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 9, no. 1 (2023): 1–13. https://doi.org/10.5281/zenodo.15087171.

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Machine Learning (ML) is revolutionizing industries by enabling intelligent decision-making and automation. However, deploying ML models in modern cloud-native applications requires scalable, maintainable, and efficient architectural patterns. This paper explores architectural patterns that facilitate the seamless integration of ML into microservices and cloud-based ecosystems. It discusses various deployment models, including ML Model as a Service (MaaS), Event-Driven ML, Federated Learning, and Serverless ML, highlighting their advantages, challenges, and best practices.The paper delves into

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Pushkar, Mehendale. "Scalable Architecture for Machine Learning Applications." Journal of Scientific and Engineering Research 11, no. 8 (2024): 111–17. https://doi.org/10.5281/zenodo.13753585.

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In the realm of Machine Learning (ML) applications, scalable architectures are crucial for addressing the challenges posed by large-scale ML tasks. This paper explores the integration of distributed computing and cloud infrastructures to ensure scalability, efficiency, and reliability while maintaining optimal performance and cost-effectiveness. It compares different cloud platforms, evaluates design patterns and architectural strategies, presents case studies from real-world ML deployments, and analyzes emerging technologies shaping the landscape of ML in the cloud. The paper concludes by pro

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Pavankumar Yanamadala. "Demystifying cloud-native enterprise architecture: A framework for digital transformation in complex organizations." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 1919–28. https://doi.org/10.30574/wjarr.2025.26.1.1231.

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This article presents a comprehensive framework for adopting cloud-native architecture within enterprise environments, addressing the significant challenges organizations face during digital transformation initiatives. The article examines the fundamental components of cloud-native systems—including containerization, microservices, and service mesh implementations—and their interconnections within a holistic architectural approach. Drawing from extensive industry implementation experiences, the article identifies critical patterns for migrating traditional enterprise architectures to distribut

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G Renugadevi, M L Sharvesh, S Subhashini, and V S Vaishaal Krishna. "Scalable Cloud Execution Engines." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 10 (2024): 2521–28. http://dx.doi.org/10.47392/irjaeh.2024.0346.

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Scalability remains a major concern for many organizations, and as technology evolves expeditiously, the number of users utilizing it also increases rapidly. In this paper, we propose a novel approach to address this challenge through the implementation of a scalable cloud execution engine using a microservices architecture. By using this design we can achieve a system with loosely coupled and independently deployable methods. Also through this, we can achieve enhanced flexibility, scalability, and reusability in our application. Through experimenting with various execution engines it is evide

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Bhargav Mallampati. "Demystifying cloud-native microservices architecture for scalable applications." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1806–17. https://doi.org/10.30574/wjaets.2025.15.1.0422.

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Cloud-native microservices architecture represents a transformational shift in software development, enabling organizations to build resilient, scalable applications specifically designed for cloud environments through decomposed, independently deployable services. This architectural paradigm leverages cloud infrastructure capabilities including elastic scaling, self-healing, and managed services while emphasizing container-based deployments and orchestration platforms. Implementation rates are surging as enterprises recognize substantial benefits in resilience, time-to-market, and operational

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Vineel Muppa. "Cloud-native event processing: Designing scalable and resilient event-driven systems." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1053–63. https://doi.org/10.30574/wjaets.2025.15.1.0217.

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This article examines the principles and implementation strategies for event-driven cloud solutions, addressing the growing need for responsive, resilient, and automated systems in modern digital enterprises. The article presents a comprehensive analysis of event-driven architecture (EDA) patterns and their integration with cloud-native technologies, exploring the synergies between messaging systems, event brokers, and serverless computing frameworks. The article outlines architectural approaches for achieving optimal performance, fault tolerance, and operational efficiency while managing the

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Debski, Andrzej, Bartlomiej Szczepanik, Maciej Malawski, Stefan Spahr, and Dirk Muthig. "A Scalable, Reactive Architecture for Cloud Applications." IEEE Software 35, no. 2 (2018): 62–71. http://dx.doi.org/10.1109/ms.2017.265095722.

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Nikhil, Bhagat. "Optimizing Performance, Cost-Efficiency, and Flexibility through Hybrid Multi-Cloud Architectures." Journal of Scientific and Engineering Research 11, no. 4 (2024): 372–79. https://doi.org/10.5281/zenodo.14273093.

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Cloud Computing is the foundation of every modern company that is scalable, adaptable and economical. Hybrid multi-cloud environments, which combine private clouds, public clouds, and multiple cloud providers, represent the next generation for scaling cloud infrastructures. Hybrid cloud architecture lets organizations reap the security and control benefits of a private cloud while also taking advantage of the scalability and cost efficiency of a public cloud. Meanwhile, multi-cloud models avoid vendor lock-in, provide risk mitigation, and enable organizations to choose the best options from mu

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Singh, Mantu. "Implementing Service Mesh Architecture for Scalable Applications." American Journal of Engineering and Technology 7, no. 4 (2025): 157–65. https://doi.org/10.37547/tajet/volume07issue04-21.

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This study examines a decentralized approach to implementing a service mesh for microservice-based systems designed for scalable data processing. Unlike traditional solutions dominated by the pipes-and-filters pattern and a centralized control plane, this approach utilizes the concept of Eblocks—unified modules that incorporate service discovery, authentication, monitoring, and load management components. This allows for the formation of various patterns (manager-worker, divide-and-conquer, hybrid models) directly at the microservice level without strict dependence on centralized logic. It is

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Thota, Praveen Kumar. "Serverless Automation Framework for Scalable and Fault-Tolerant Cloud Operations." International Scientific Journal of Engineering and Management 01, no. 02 (2022): 1–8. https://doi.org/10.55041/isjem00139.

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Progress in cloud computing has resulted in different ways of building, rolling out, and maintaining today’s applications. Serverless computing, which emerged during this period, takes care of managing servers and allows developers to focus only on their code and what it does. The paper looks carefully at a serverless automation framework capable of ensuring scalability and fault tolerance in dynamic cloud. With serverless computing, users rely on event-based architectures and can take advantage of cloud services that handle scaling, keep the system running, and manage billing which is a major

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Lathar, Pankaj. "Efficient Access of Data Resources in Cloud." International Journal of Information Communication Technologies and Human Development 10, no. 2 (2018): 28–41. http://dx.doi.org/10.4018/ijicthd.2018040103.

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An attempt towards developing time efficient cloud computing architecture, by considering the deficiencies with respect to existing clouds, better ontology-based cloud information architecture is proposed in this thesis. In this architecture, additional modules on query retrieval and query refinement are added for better performance. Rocchio technique is used for query refinement to extract results with respect to relevance criterion is adopted. The proposed architecture gives better-indexed results after transforming the user query. Further, the cloud customers are provided with a flexible, s

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Vikas, Prajapati. "Cloud-Based Database Management: Architecture, Security, challenges and solutions." Journal of Global Research in Electronics and Communications (JGREC) 1, no. 1 (2025): 07–13. https://doi.org/10.5281/zenodo.14934833.

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The rapid evolution of cloud computing has revolutionized database management, offering scalable, flexible, and cost-effective solutions for managing large volumes of data. Cloud computing has transformed database management by providing scalable, flexible, and cost-effective solutions for handling massive volumes of data. Cloud-Based Database Management Systems (CDBMS) eliminate the need for extensive on-premise infrastructure, enabling organizations to focus on core operations. These systems leverage advanced architectures, including layered designs that enhance scalability, interoperability

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Anwar, Ashif. "Event-Driven Architecture in Distributed Systems: Leveraging Azure Cloud Services for Scalable Applications." European Journal of Computer Science and Information Technology 13, no. 29 (2025): 13–27. https://doi.org/10.37745/ejcsit.2013/vol13n291327.

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Event-driven architecture (EDA) represents a transformative paradigm in distributed systems development, enabling organizations to build more responsive, scalable, and resilient applications. By facilitating asynchronous communication through events that represent significant state changes, EDA establishes loosely coupled relationships between system components that can operate independently. This architectural approach addresses fundamental challenges in distributed systems including component coordination, state management, and fault isolation. Microsoft Azure cloud services provide comprehe

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Emily, R. Walker. "Hybrid Cloud Architectures for Scalable and Secure Data Storage." International Journal of Innovative Computer Science and IT Research 01, no. 02 (2025): 12–23. https://doi.org/10.5281/zenodo.15147313.

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As far as the requirements of scalable and secure data storage solutions in the past few years are concerned, their requirements have always remained a top concern for businesses with an increasing amount of data to deal with. Hybrid cloud architectures, providing private and public cloud infrastructure, possess a promising solution towards scalability, flexibility, and increased security. This paper is an examination of the building blocks of hybrid cloud systems with regard to their ability to support scalable data storage as well as deal with the issue of secur

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Natarajan, Loganandh. "Optimizing Cloud Architecture for Scalable Data Analytics and Advanced Data Science Capabilities." International Journal of Engineering and Computer Science 13, no. 12 (2024): 26677–97. https://doi.org/10.18535/ijecs/v13i12.4954.

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The relatively short timeframe of the data-oriented approach has made cloud architecture the basis for flexible and effective data analysis and data science projects. This paper presents the design strategies and considerations of cloud architectures for data science platforms that compliments modern analytics and machine learning workloads. Sub-processes like data acquisition, management, analysis, and coordination are discussed, as well as their part in supporting moment and science driven decision-making. Responsiveness is given on the use of tools and platforms that are built natively on c

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Researcher. "BUILDING SCALABLE CLOUD UI APPLICATIONS: 5 KEY ARCHITECTURAL PRINCIPLES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 1043–53. https://doi.org/10.5281/zenodo.14009229.

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This article explores the fundamental principles underlying scalable cloud UI applications in the rapidly evolving landscape of cloud computing. It examines five key architectural principles: elastic scalability, microservices architecture, cross-platform compatibility, automated testing and deployment, and security and compliance. The article discusses the importance of these principles in meeting the challenges posed by exponential market growth, increasing data volumes, and evolving user expectations. It provides insights into implementing these principles, their benefits, and the challenge

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Thakur, Rahul Singh. "Scalable Cloud Architectures: Sharding Services for High Availability." European Journal of Computer Science and Information Technology 13, no. 34 (2025): 88–96. https://doi.org/10.37745/ejcsit.2013/vol13n348896.

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Service sharding has emerged as a critical architecture pattern for achieving high availability in modern cloud environments where traditional monolithic systems fail to meet scalability demands. This article presents a comprehensive framework for implementing service sharding across distributed infrastructures, detailing both technical benefits and operational challenges. The distributed nature of sharded architectures enables organizations to contain failures within limited blast radii, significantly enhancing system resilience during infrastructure disruptions. Through the proper implementa

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Rohit Laheri. "Designing Secure and Scalable Cloud Infrastructures using Azure Landing Zones." Journal of Information Systems Engineering and Management 10, no. 49s (2025): 1116–26. https://doi.org/10.52783/jisem.v10i49s.10052.

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Due to their flexibility, scalability, and efficiency, cloud computing is now paramount for enterprises in the modern era. Although organizations on the cloud can take advantage of these benefits, deploying compliant, scalable, and manageable cloud environments is still a complex undertaking. Azure Landing Zones provide a well-structured framework to aid organizations in navigating these challenges by providing foundational architecture for cloud adoption. This paper will feature the architectural principles behind Azure Landing Zones, their components, best practices for implementation and op

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Researcher. "SCALABLE AI-DRIVEN MICROSERVICES ARCHITECTURES FOR DISTRIBUTED CLOUD ENVIRONMENTS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 154–68. https://doi.org/10.5281/zenodo.14053729.

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This article presents a comprehensive approach to designing scalable AI-driven microservices architectures for distributed cloud environments. It explores key challenges in integrating AI into distributed systems and proposes strategies for microservices design, deployment, and scaling of AI workloads. The article covers data pipeline optimization, security, and compliance considerations and presents a detailed case study of a scalable image recognition service. Through analysis of scalability, efficiency, and robustness, the proposed architecture demonstrates significant improvements over tra

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Dileep Domakonda. "Secure and Scalable Microservices Architecture : Principles, Benefits, and Challenges." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1897–902. https://doi.org/10.32628/cseit23112569.

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Microservices architecture is one approach to structuring applications as a collection of small, independently deployable services interacting via APIs, which improves modularity, scalability, and fault isolation. Microservices provide better resilience, deployment flexibility, and utilization of resources compared to monolithic architectures, making them a perfect fit for cloud-native applications. In today's paper, we discuss fundamental principles such as independent deployment, decoupling, fault tolerance, and technology agnosticism while considering challenges such as inter-service commun

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Researcher. "ENHANCING IOT SYSTEMS WITH SCALABLE CLOUD ARCHITECTURES FOR REAL-TIME DATA PROCESSING." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 774–86. https://doi.org/10.5281/zenodo.14229605.

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The development and application of Internet of Things (IoT) systems coupled with scalable cloud architectures for real-time data processing are examined in detail in this extensive essay. From data ingestion to security concerns, the paper explores the basic difficulties enterprises face while overseeing extensive IoT deployments. It explores the elements of cloud-based architecture, highlighting the crucial roles played by processing frameworks, storage options, and data intake levels. The essay discusses edge analytics integration and emphasizes how it can improve privacy, optimize bandwidth

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Dhanorkar, Tejas, Sai Charan Ponnoju, and Shemeer Sulaiman Kunju. "Cloud-Native Wallet Fabric: Engineering Scalable, Multicurrency e-Wallet Platforms." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 6, no. 1 (2024): 766–76. https://doi.org/10.60087/jaigs.v6i1.368.

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The rapid evolution of digital financial ecosystems demands e-wallet platforms that can seamlessly scale while supporting diverse currencies and high transaction volumes. Traditional e-wallet systems, often constrained by monolithic architectures, struggle to meet these requirements, particularly in global, multi-currency contexts. This paper introduces the Cloud-Native Wallet Fabric (CNWF), a novel architecture designed to address these challenges through cloud-native technologies. By leveraging microservices, containerization, Kubernetes orchestration, and serverless computing, CNWF ensures

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Bharat Kumar Reddy Kallem. "Building a scalable enterprise data architecture for financial institutions." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1153–57. https://doi.org/10.30574/wjaets.2025.15.1.0249.

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Enterprise data architecture for financial institutions has evolved dramatically to address the exponential growth of financial data, which now exceeds 2.5 exabytes daily with a 40% annual growth rate. Traditional infrastructures struggle to meet modern operational demands, with a significant majority of institutions reporting scaling challenges. The shift toward real-time processing requirements compounds these difficulties as banking systems process billions of transactions daily while investment platforms handle hundreds of thousands of market data messages per second during volatility even

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Wang, Rui, Shi Ying, Meiyan Li, and Shun Jia. "HSACMA: a hierarchical scalable adaptive cloud monitoring architecture." Software Quality Journal 28, no. 3 (2020): 1379–410. http://dx.doi.org/10.1007/s11219-020-09524-z.

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Prabu, Arjunan. "Serverless Computing in Google Cloud Platform." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 10, no. 2 (2022): 1–4. https://doi.org/10.5281/zenodo.14607960.

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Serverless computing represents a paradigm shift in Cloud architecture, changing the paradigm of how organizations build and deploy applications [1]. I present in this technical paper serverless computing infrastructure of GCP in terms of core component, architectural patterns, and implementation methodologies. I do performance analysis and real-world scenario implementation to demonstrate how to use GCP's offerings for serverless computing to devise highly scalable, cost-efficient solutions with operational efficiency. It includes architectural considerations, and guidelines of practical impl

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Saurabh Ashwinikumar Dave, Sivaprasad Nadukuru, Swetha Singiri, Om Goel, Ojaswin Tharan, and Prof.(Dr.) Arpit Jain. "Scalable Microservices for Cloud Based Distributed Systems." Darpan International Research Analysis 12, no. 3 (2024): 776–809. http://dx.doi.org/10.36676/dira.v12.i3.132.

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In the evolving landscape of cloud computing, scalable microservices have emerged as a pivotal architecture for developing distributed systems. This approach facilitates the decomposition of applications into smaller, independently deployable services, allowing for greater agility and scalability. This paper explores the essential principles of microservices architecture, highlighting its advantages over monolithic systems, such as improved fault isolation, enhanced scalability, and streamlined continuous integration and deployment processes. We examine the key design patterns and technologies

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Bharath Kumar Reddy Janumpally. "Event-Driven Serverless Architectures for High-Scale Customer Support: An Empirical Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1285–94. https://doi.org/10.32628/cseit251112133.

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Serverless computing has emerged as a transformative paradigm for building scalable cloud applications, yet its application in customer support systems remains largely unexplored. This article presents a comprehensive analysis of a cloud-native serverless architecture designed to address the scalability challenges inherent in modern customer support operations. The article proposes an event-driven framework that leverages Function-as-a-Service (FaaS) platforms across major cloud providers, demonstrating significant improvements in resource utilization and cost efficiency compared to traditiona

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Bhaskar Goyal. "Understanding cloud-native AI: The foundation of scalable platform architecture." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 822–27. https://doi.org/10.30574/wjaets.2025.15.1.0251.

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Cloud-native AI represents a transformative paradigm shift in enterprise artificial intelligence deployment, fundamentally reimagining how organizations architect, deploy, and manage AI systems. By embracing containerization, microservices architecture, and declarative configuration, this approach enables unprecedented levels of scalability, resilience, and operational efficiency. The integration of Kubernetes orchestration with specialized hardware management creates a foundation for dynamically scaling AI workloads while optimizing resource utilization. Organizations implementing these archi

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Himaja Sabbineni. "Architecting Scalable Cloud Systems: Advanced API Integration Strategies and Best Practices." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2117–24. https://doi.org/10.32628/cseit251112172.

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This comprehensive article explores the evolution and implementation of scalable cloud architectures with advanced API integration strategies in modern enterprise environments. The article explores fundamental aspects of distributed computing, container orchestration, and high-availability patterns while examining their impact on system performance and business outcomes. Through an analysis of current industry practices, the article demonstrates how organizations leverage various API protocols, performance optimization techniques, and security measures to build resilient cloud infrastructures.

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Venkata, Gummadi. "Designing a Scalable Architecture for Customer Data Engineering Platform on Cloud Infrastructure: A Comprehensive Framework." Journal of Scientific and Engineering Research 10, no. 12 (2023): 243–51. https://doi.org/10.5281/zenodo.14012383.

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The exponential growth of customer data in modern enterprises has created unprecedented challenges in data engineering, necessitating architectures capable of handling petabyte-scale processing while maintaining real-time analytics capabilities. This paper presents a comprehensive architectural framework for designing and implementing scalable customer data engineering platforms utilizing cloud infrastructure. The proposed architecture addresses critical challenges including real-time data processing, horizontal scalability, data governance, and security considerations. Through rigorous experi

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Lingkau, Mingalu Pangare, Kuo Ling Haoseng, Mingalu Pangare Lingkau, Mingalu Pangare Lingkau, and Yong Meng Phaotangu. "Healthcare and IoT devices: role of information technology in the healthcare industry." Business & IT XII, no. 1 (2022): 169–76. http://dx.doi.org/10.14311/bit.2022.01.20.

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Today, wearable health products play a crucial role in most locations, such as constant wellness monitoring of people, street traffic management, weather forecasting, along with smart house. These sensor devices constantly generate massive amounts of data and are kept in cloud computing. This particular chapter proposes Internet of Things design to store and system scalable sensor information for healthcare apps. Proposed architecture comprises 2 primary architecture, specifically, MetaFog-Redirection and Choosing and Grouping architecture. Though cloud computing offers scalable data storage,

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Mayoral, Arturo, Raül Muñoz, Ricard Vilalta, Ramon Casellas, Ricardo Martínez, and Víctor López. "Need for a Transport API in 5G for Global Orchestration of Cloud and Networks Through a Virtualized Infrastructure Manager and Planner." Journal of Optical Communications and Networking 9, no. 1 (2017): A55—A62. https://doi.org/10.1364/JOCN.9.000A55.

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The new 5G paradigm seeks for a scalable architecture that is able to efficiently manage the increasing volume of traffic generated by smart devices to be processed in a distributed cloud infrastructure. To this end, coordinated management of the network and the cloud resources forming an end-to-end system is of great importance. Software defined networking and network function virtualization architectures are the key enablers for integrating network and cloud resources, enabling cross optimization on both sides. This optimization requires efficient resource allocation algorithms, which take i

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Oyekunle Claudius Oyeniran, Adebunmi Okechukwu Adewusi, Adams Gbolahan Adeleke, Lucy Anthony Akwawa, and Chidimma Francisca Azubuko. "Microservices architecture in cloud-native applications: Design patterns and scalability." Computer Science & IT Research Journal 5, no. 9 (2024): 2107–24. http://dx.doi.org/10.51594/csitrj.v5i9.1554.

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Microservices architecture has emerged as a pivotal approach for designing scalable and maintainable cloud-native applications. Unlike traditional monolithic architectures, microservices decompose applications into small, independently deployable services that communicate through well-defined APIs. This architectural shift enhances modularity, allowing for improved scalability, resilience, and flexibility. This paper explores the core concepts of microservices, including service decomposition, inter-service communication, and data management. It delves into key design patterns such as the API

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Gupta, Vicky Kumar. "Java Full Stack Development for Robust and Scalable Enterprise Architecture." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03417.

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Abstract: In the era of digital transformation, enterprises demand robust, scalable, and maintainable software architectures that can support dynamic business needs. Java Full Stack Development has emerged as a comprehensive approach to address these challenges, integrating frontend, backend, and database technologies to deliver end-to-end enterprise solutions. This paper explores the key components and best practices in Java Full Stack Development, including the use of modern frontend frameworks (e.g., Angular, React), backend technologies (e.g., Spring Boot, RESTful APIs), and databases (SQL

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Jyoti Aggarwal. "ETL pipelines for cloud-native data platforms: Architecting real-time analytics on integrated cloud services." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 107–14. https://doi.org/10.30574/wjaets.2025.15.2.0522.

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This article presents a comprehensive overview of ETL (Extract, Transform, Load) pipelines in cloud-native data platforms, focusing on their architecture and implementation for real-time analytics. It examines how traditional batch-oriented ETL processes have evolved into dynamic, on-demand systems that leverage cloud capabilities to deliver timely insights with enhanced efficiency and reduced operational costs. The discussion covers fundamental components of cloud-native ETL architecture, strategies for real-time data ingestion and transformation, workflow orchestration techniques, and approa

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Belli, Laura, Simone Cirani, Luca Davoli, et al. "A Scalable Big Stream Cloud Architecture for the Internet of Things." International Journal of Systems and Service-Oriented Engineering 5, no. 4 (2015): 26–53. http://dx.doi.org/10.4018/ijssoe.2015100102.

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The Internet of Things (IoT) will consist of billions (50 billions by 2020) of interconnected heterogeneous devices denoted as “Smart Objects:” tiny, constrained devices which are going to be pervasively deployed in several contexts. To meet low-latency requirements, IoT applications must rely on specific architectures designed to handle the gigantic stream of data coming from Smart Objects. This paper propose a novel Cloud architecture for Big Stream applications that can efficiently handle data coming from Smart Objects through a Graph-based processing platform and deliver processed data to

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Dineva, Kristina, and Tatiana Atanasova. "Design of Scalable IoT Architecture Based on AWS for Smart Livestock." Animals 11, no. 9 (2021): 2697. http://dx.doi.org/10.3390/ani11092697.

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In the ecological future of the planet, intelligent agriculture relies on CPS and IoT to free up human resources and increase production efficiency. Due to the growing number of connected IoT devices, the maximum scalability capacity, and available computing power of the existing architectural frameworks will be reached. This necessitates finding a solution that meets the continuously growing demands in smart farming. Cloud-based IoT solutions are achieving increasingly high popularity. The aim of this study was to design a scalable cloud-based architecture for a smart livestock monitoring sys

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Barik, Rabindra K., Rojalina Priyadarshini, Rakesh K. Lenka, Harishchandra Dubey, and Kunal Mankodiya. "Fog Computing Architecture for Scalable Processing of Geospatial Big Data." International Journal of Applied Geospatial Research 11, no. 1 (2020): 1–20. http://dx.doi.org/10.4018/ijagr.2020010101.

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Geospatial data analysis using cloud computing platform is one of the promising areas for analysing, retrieving, and processing volumetric data. Fog computing paradigm assists cloud platform where fog devices try to increase the throughput and reduce latency at the edge of the client. In this research paper, the authors discuss two case studies on geospatial data analysis using Fog-assisted cloud computing namely, (1)Ganga River Basin Management System; and (2)Tourism Information Management of India. Both case studies evaluate proposed GeoFog architecture for efficient analysis and management

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Mounika, Kothapalli. "Cloud Computing and Serverless Architecture Utilization." European Journal of Advances in Engineering and Technology 6, no. 2 (2019): 88–92. https://doi.org/10.5281/zenodo.12770674.

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Cloud computing has rapidly changed how software development works, with the increasing popularity of serverless architectures. This article explores the role that cloud services and serverless computing play, in making application development scalable and efficient. Serverless computing streamlines the development process by handling servers behind the scenes and using a pay per use model reducing the burden on developers to maintain servers. This shift does not accelerate application creation. Also simplifies the complexities associated with deploying software on a large scale. Moreover, com

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Mohapatra, Subasish, Banshidhar Majhi, and Srikanta Patnaik. "Scalable architecture for ubiquitous healthcare using sensor cloud platform." International Journal of Information and Communication Technology 6, no. 2 (2014): 156. http://dx.doi.org/10.1504/ijict.2014.060395.

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Xu, Yi, and Abdelsalam Helal. "Scalable Cloud–Sensor Architecture for the Internet of Things." IEEE Internet of Things Journal 3, no. 3 (2016): 285–98. http://dx.doi.org/10.1109/jiot.2015.2455555.

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Ashutosh Verma. "A comparative analysis of cloud providers for scalable and reliable systems." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2520–29. https://doi.org/10.30574/wjaets.2025.15.2.0809.

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This article presents a comparative assessment of major cloud providers (AWS, Microsoft Azure, and Google Cloud Platform) for building scalable and reliable systems in today's digital landscape. It evaluates the architecture, features, and economic considerations that influence cloud adoption decisions across enterprises. The comparison encompasses three critical dimensions: scalability capabilities including auto-scaling mechanisms and containerization support; reliability features such as high availability architectures and fault isolation boundaries; and pricing models with cost optimizatio

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Singu, Santosh Kumar. "Leveraging Snowflake for Scalable Financial Data Warehousing." International Journal of Computing and Engineering 6, no. 5 (2024): 41–51. http://dx.doi.org/10.47941/ijce.2296.

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Purpose: The study discusses the increasing challenges faced by financial services due to fast-growing transaction, regulatory, and client data, and the need for more flexible, scalable, and affordable data management systems. It examines the potential of Snowflake, a cloud-based data warehousing platform, to address these issues through its multi-cluster shared data architecture Methodology: The paper analyzes Snowflake's architecture, focusing on its ability to decouple storage from compute, allowing organizations to scale resources as needed. Case studies of financial institutions implement

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Researcher. "CLOUD ADAPTATION: NAVIGATING THE MIGRATION LANDSCAPE." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 874–81. https://doi.org/10.5281/zenodo.13929366.

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This article explores the accelerating trend of organizations transitioning from traditional datacenter architectures to cloud-based solutions, driven by the expanding global cloud computing market and the need for flexible, scalable IT infrastructure. It examines key considerations for cloud migration, including workload characteristics, regulatory compliance, cost optimization, performance requirements, security considerations, and existing IT infrastructure. The article delves into critical aspects such as cloud security, data compliance, and cost and performance optimization, providing ins

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Carlin, Sean, and Kevin Curran. "Cloud Computing Security." International Journal of Ambient Computing and Intelligence 3, no. 1 (2011): 14–19. http://dx.doi.org/10.4018/jaci.2011010102.

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In this paper, the authors focus on Cloud Computing, which is a distributed architecture that centralizes server resources on quite a scalable platform so as to provide on demand’ computing resources and services The authors outline what cloud computing is, the various cloud deployment models and the main security risks and issues that are currently present within the cloud computing industry.

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Moskaleva, Iu P., Z. S. Seidametova, and V. A. Temnenko. "Cloud architecture and its implementations for universities." CTE Workshop Proceedings 1 (March 21, 2013): 63–65. http://dx.doi.org/10.55056/cte.93.

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Cloud technologies are radically changing the way we think about how computing applications and services can be used in building business models, management, education, and more. Large data centers make it possible to allocate resources for sharing through application hosting at both the hardware and software level. Services can be scalable and, due to their elasticity, can be provided depending on the requirements and needs of customers. The pay-as-you-go model works.

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Naayini, Prudhvi. "Scalable AI Model Deployment and Management on Serverless Cloud Architecture." International Journal of Electrical, Electronics and Computers 9, no. 1 (2024): 1–12. https://doi.org/10.22161/eec.91.1.

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Scalable deployment of deep learning models in the cloud faces challenges in balancing performance, cost, and manageability. This paper investigates serverless cloud architecture for AI model inference, focusing on AWS technologies such as AWS Lambda, API Gateway, and Kubernetes-based serverless extensions (e.g., AWS EKS with Knative). We first outline the limitations of traditional, server-based model hosting to motivate the serverless approach. Then, we present novel strategies for scalable model serving: an adaptive resource provisioning algorithm, intelligent model caching, and efficient m

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Panchalingala, Ajay Kumar. "AWS Cloud Architecture: A Comprehensive Analysis of Best Practices and Design Principles." European Journal of Computer Science and Information Technology 13, no. 37 (2025): 109–16. https://doi.org/10.37745/ejcsit.2013/vol13n37109116.

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This comprehensive article examines the fundamental principles and best practices of AWS cloud architecture, focusing on how organizations can leverage AWS services to build robust, scalable, and cost-effective solutions. The article analyzes the implementation of the AWS Well-Architected Framework, advanced architectural patterns, and security measures across multiple enterprise deployments. Through systematic examination of microservices, serverless computing, and security implementations, this article demonstrates how proper architectural designs significantly improve resource utilization,

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