Academic literature on the topic 'Cloud Native Architecture'

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Journal articles on the topic "Cloud Native Architecture"

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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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Duan, Qiang. "Intelligent and Autonomous Management in Cloud-Native Future Networks—A Survey on Related Standards from an Architectural Perspective." Future Internet 13, no. 2 (2021): 42. http://dx.doi.org/10.3390/fi13020042.

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Cloud-native network design, which leverages network virtualization and softwarization together with the service-oriented architectural principle, is transforming communication networks to a versatile platform for converged network-cloud/edge service provisioning. Intelligent and autonomous management is one of the most challenging issues in cloud-native future networks, and a wide range of machine learning (ML)-based technologies have been proposed for addressing different aspects of the management challenge. It becomes critical that the various management technologies are applied on the foun
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Bhole, Anju. "Cloud-Native Architecture and Microservices." Journal of Artificial Intelligence, Machine Learning and Data Science 1, no. 2 (2023): 2032–37. https://doi.org/10.51219/jaimld/anju-bhole/447.

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Ramadevi Sannapureddy. "Cloud-Native Enterprise Integration: Architectures, Challenges, and Best Practices." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 167–73. https://doi.org/10.32996/jcsts.2025.7.5.22.

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Cloud-native enterprise integration represents a transformative shift from monolithic middleware to distributed, loosely-coupled architectures that enable organizations to achieve greater business agility and operational efficiency. This article examines the architectural patterns, challenges, and best practices for successful cloud-native integration implementations. By leveraging event-driven architectures, API-first approaches, service meshes, and hybrid integration models, enterprises can create flexible, resilient integration solutions that support modern business requirements. However, t
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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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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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Researcher. "MODERNIZING LEGACY BIGDATA SYSTEMS: A CLOUD-NATIVE MIGRATION FRAMEWORK AND CASE STUDIES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 1409–18. https://doi.org/10.5281/zenodo.14501045.

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This comprehensive article examines the transformation of legacy big data systems to cloud-native architectures, focusing on migration strategies, implementation frameworks, and real-world case studies. The article investigates the evolution of big data architectures across three generations, from traditional data warehouses to modern cloud-native solutions, highlighting the critical factors driving modernization initiatives. The article analyzes cloud-native architecture principles, high-availability strategies, and migration frameworks while presenting detailed case studies from industry lea
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Prashant Chaturvedi. "Securing Cloud-Native Applications: A Comprehensive Guide to Modern Challenges and Solutions." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2534–42. https://doi.org/10.32628/cseit251112255.

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Cloud-native architecture has fundamentally transformed enterprise technology landscapes, introducing complex security challenges that traditional approaches struggle to address. This technical article examines the evolution of cloud-native security, focusing on container vulnerabilities, service mesh complexity, and resource configuration management. The article explores emerging solutions, including zero-trust architectures, automated security controls, and advanced observability frameworks. Through analysis of implementation case studies and industry research, the article demonstrates the e
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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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Premjit Paul Ger. "Designing cloud-native architectures for financial system resilience." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1081–87. https://doi.org/10.30574/wjaets.2025.15.3.1016.

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This paper presents a transformative framework for cloud-native architectures in financial services, demonstrating how microservices, containerization, and declarative infrastructure fundamentally reshape enterprise financial systems. The architecture enables institutions to address critical challenges, including regulatory compliance, scalability requirements, and fault tolerance, while facilitating accelerated innovation cycles. Through examination of implementation strategies across global financial organizations, the paper illustrates how cloud-native principles enhance operational resilie
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Dissertations / Theses on the topic "Cloud Native Architecture"

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Arora, Sagar. "Cloud Native Network Slice Orchestration in 5G and Beyond." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS278.

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La virtualisation des fonctions réseau (NFV) est le pilier fondateur de l'architecture 5G basée sur les services. La NFV a débuté en 2012, avec les fonctions de réseau virtuelles (VNF) basées sur les machines virtuelles (VM). Les conteneurs sont devenus une technologie alternative de conditionnement intéressante pour la virtualisation des fonctions réseau. Le conteneur est léger en termes de consommation de ressources ce qui améliore son temps d'instanciation. Outre les fonctions de réseau, la conteneurisation peut être un outil prometteur pour les applications multi-access edge computing (MEC
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Widerberg, Anton, and Erik Johansson. "Observability of Cloud Native Systems: : An industrial case study of system comprehension with Prometheus & knowledge transfer." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-22019.

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Background:                                                                                                            Acquiring comprehension and observability of software systems is a vital and necessary activity for testing and maintenance; however, these tasks are time-consuming for engineers. Concurrently cloud computing requires microservices to enhance the utilization of cloudnative deployment, which simultaneously introduces a high degree of complexity. Further,codifying and distributing technical knowledge within the organization has been proven to be vital for both competitiveness an
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Mara, Jösch Ronja. "Managing Microservices with a Service Mesh : An implementation of a service mesh with Kubernetes and Istio." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280407.

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The adoption of microservices facilitates extending computer systems in size, complexity, and distribution. Alongside their benefits, they introduce the possibility of partial failures. Besides focusing on the business logic, developers have to tackle cross-cutting concerns of service-to-service communication which now defines the applications' reliability and performance. Currently, developers use libraries embedded into the application code to address these concerns. However, this increases the complexity of the code and requires the maintenance and management of various libraries. The servi
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de, la Rúa Martínez Javier. "Scalable Architecture for Automating Machine Learning Model Monitoring." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280345.

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Last years, due to the advent of more sophisticated tools for exploratory data analysis, data management, Machine Learning (ML) model training and model serving into production, the concept of MLOps has gained more popularity. As an effort to bring DevOps processes to the ML lifecycle, MLOps aims at more automation in the execution of diverse and repetitive tasks along the cycle and at smoother interoperability between teams and tools involved. In this context, the main cloud providers have built their own ML platforms [4, 34, 61], offered as services in their cloud solutions. Moreover, multip
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Mrowczynski, Piotr. "Scaling cloud-native Apache Spark on Kubernetes for workloads in external storages." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237455.

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CERN Scalable Analytics Section currently offers shared YARN clusters to its users as monitoring, security and experiment operations. YARN clusters with data in HDFS are difficult to provision, complex to manage and resize. This imposes new data and operational challenges to satisfy future physics data processing requirements. As of 2018, there were over 250 PB of physics data stored in CERN’s mass storage called EOS. Hadoop-XRootD Connector allows to read over network data stored in CERN EOS. CERN’s on-premise private cloud based on OpenStack allows to provision on-demand compute resources. E
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Perdigão, João Miguel Tomás Ferrão Correia. "A Software Architecture for Highly Available Cloud-Native Applications." Master's thesis, 2021. http://hdl.handle.net/10316/98080.

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Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia<br>As aplicações nativas da cloud estão a tornar-se na abordagem aceite para construir sistemas. Com a sua heterogeneidade, aumento crescente de tamanho e natureza dinâmica, surgem novos desafios de forma a assegurar requisitos importantes, tais como a sua disponibilidade. Nos cenários de aplicações críticas para o negócio, no qual uma interrupção do seu funcionamento que dure um minuto tem impactos financeiros significativos, é imperativo garantir que o sistema permanece disponível. Deste modo,
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Books on the topic "Cloud Native Architecture"

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Team, FreeWheel Biz-UI. Cloud-Native Application Architecture. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-9782-2.

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Goniwada, Shivakumar R. Cloud Native Architecture and Design. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7226-8.

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Team, FreeWheel Biz-UI. Cloud-Native Application Architecture: Microservice Development Best Practice. Springer, 2023.

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Dutt, Dinesh G. Cloud Native Data Center Networking: Architecture, Protocols, and Tools. O'Reilly Media, 2019.

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Eyskens, Stéphane. Azure Cloud Native Architecture Mapbook: Explore Microsoft Cloud's Infrastructure, Application, Data, and Security Architecture. de Gruyter GmbH, Walter, 2021.

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Modern Enterprise Architecture: Using DevSecOps and Cloud-Native in Large Enterprises. Apress L. P., 2022.

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Price, Ed, and Stephane Eyskens. The Azure Cloud Native Architecture Mapbook: Explore Microsoft Cloud's infrastructure, application, data, and security architecture. Packt Publishing, 2021.

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Goniwada, Shivakumar R. Cloud Native Architecture and Design: A Handbook for Modern Day Architecture and Design with Enterprise-Grade Examples. Apress L. P., 2021.

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Bartlett, Jonathan. Cloud Native Applications with Docker and Kubernetes: Design and Build Cloud Architecture and Applications with Microservices, EMQ and Multi-Site Configurations. Apress L. P., 2022.

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Cloud Native Infrastructure: Patterns for Scalable Infrastructure and Applications in a Dynamic Environment. O'Reilly Media, 2017.

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Book chapters on the topic "Cloud Native Architecture"

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Jakóbczyk, Michał Tomasz. "Cloud-Native Architecture." In Practical Oracle Cloud Infrastructure. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5506-3_9.

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Team, FreeWheel Biz-UI. "Serverless Architecture." In Cloud-Native Application Architecture. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-9782-2_6.

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Li, Feifei, Xuan Zhou, Peng Cai, Rong Zhang, Gui Huang, and XiangWen Liu. "Architecture of Cloud-Native Database." In Cloud Native Database. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-4057-4_3.

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Goniwada, Shivakumar R. "Cloud Native Services." In Cloud Native Architecture and Design. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7226-8_2.

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Goniwada, Shivakumar R. "Cloud Native Trends." In Cloud Native Architecture and Design. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7226-8_20.

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Goniwada, Shivakumar R. "Cloud Native Architecture Principles." In Cloud Native Architecture and Design. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7226-8_3.

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Goniwada, Shivakumar R. "Cloud Native Data Architecture." In Cloud Native Architecture and Design. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7226-8_8.

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Goniwada, Shivakumar R. "Serverless Architecture." In Cloud Native Architecture and Design. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7226-8_7.

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Team, FreeWheel Biz-UI. "Continuous Integration and Continuous Deployment." In Cloud-Native Application Architecture. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-9782-2_9.

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Team, FreeWheel Biz-UI. "Service Development and Operation." In Cloud-Native Application Architecture. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-9782-2_3.

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Conference papers on the topic "Cloud Native Architecture"

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Ashe, Subrata, and Haricharan Ramachandra. "The Effect of Continuous Encryption of Data in Cloud Native Architecture." In 2024 IEEE Cloud Summit. IEEE, 2024. http://dx.doi.org/10.1109/cloud-summit61220.2024.00034.

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Borges, Maria C., and Sebastian Werner. "Continuous Observability Assurance in Cloud-Native Applications." In 2025 IEEE 22nd International Conference on Software Architecture Companion (ICSA-C). IEEE, 2025. https://doi.org/10.1109/icsa-c65153.2025.00036.

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Borges, Maria C., Joshua Bauer, and Sebastian Werner. "OXN - Automated Observability Assessments for Cloud-Native Applications." In 2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C). IEEE, 2024. http://dx.doi.org/10.1109/icsa-c63560.2024.00035.

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Sun, Bing, Hui Zhou, Guohuan Song, and Qisong Zhang. "Research on the Technology Architecture of Full-stack Cloud-Native Hosted Cloud." In 2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA). IEEE, 2024. http://dx.doi.org/10.1109/mita60795.2024.10751724.

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Wen, Jifei, Jingguo Ge, Zheyuan Zhang, Yuepeng E, and Bingzhen Wu. "A Latency-Predictable Cloud-Native Network Architecture based on XDP." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00090.

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Zeng, Desheng, Delan Zeng, Shuanglong Pang, Xiaodan Chen, Liyi Liu, and Shuai Shao. "A Design of Storage-computation Separation Architecture for Cloud-native Environments." In 2024 5th International Conference on Information Science and Education (ICISE-IE). IEEE, 2024. https://doi.org/10.1109/icise-ie64355.2024.11025501.

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Juhlin, Prerna, Rosario Othen, Andreas Zehnpfund, et al. "Open Reference Architecture for Sustainable Papermaking based on Industrial Interoperability Standards and Cloud-Native Technologies." In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2024. http://dx.doi.org/10.1109/etfa61755.2024.10711031.

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Li, Yan, Heng Hu, Shude Yang, and Wei Zhang. "Research on the Automated Deployment Inspection System for Business Systems Based on Cloud-Native Architecture." In 2024 IEEE 4th International Conference on Data Science and Computer Application (ICDSCA). IEEE, 2024. https://doi.org/10.1109/icdsca63855.2024.10860154.

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Saravanan, S., and B. Selva Priya. "Enhancing 5G RAN Agility: Cloud-Native Architectures For Rapid Deployment And Maintenance." In 2025 International Conference on Advanced Computing Technologies (ICoACT). IEEE, 2025. https://doi.org/10.1109/icoact63339.2025.11004829.

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Jung, Young H., Subhash Talluri, and Imen Grida Ben Yahya. "A Path to Autonomous Network Operation for Future Mobile Network using GenAI and AI/ML." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.th3a.3.

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The evolution of mobile networks demands increasingly sophisticated approaches to network operation and resource management. While current architectures have improved through function decomposition and service-based designs, achieving true autonomy in network operations remains challenging. This paper presents a framework leveraging AI/ML and generative AI capabilities to enable the path toward autonomous network operations, demonstrating how cloud-native implementation enables intelligent scaling and self-optimization while reducing operational complexity.
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Reports on the topic "Cloud Native Architecture"

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Chandramouli, Ramaswamy. A Zero Trust Architecture Model for Access Control in Cloud Native Applications in Multi-Cloud Environments. National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.sp.800-207a.ipd.

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Chandramouli, Ramaswamy. A Zero Trust Architecture Model for Access Control in Cloud-Native Applications in Multi-Location Environments. National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.sp.800-207a.

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Leathers, Emily, Clayton Thurmer, and Kendall Niles. Encryption for edge computing applications. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48596.

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As smart sensors and the Internet of Things (IoT) exponentially expand, there is an increased need for effective processing solutions for sensor node data located in the operational arena where it can be leveraged for immediate decision support. Current developments reveal that edge computing, where processing and storage are performed close to data generation locations, can meet this need (Ahmed and Ahmed 2016). Edge computing imparts greater flexibility than that experienced in cloud computing architectures (Khan et al. 2019). Despite these benefits, the literature highlights open security i
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