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Journal articles on the topic 'Orchestration cloud native'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Pasupuleti, Murali Krishna. "Container Orchestration in Multi-Cloud Environments: A Performance Evaluation." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 06 (2025): 327–40. https://doi.org/10.62311/nesx/rphcrcscrcec2.

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The emergence of multi-cloud strategies has significantly transformed how enterprises deploy and manage applications, particularly through the use of container orchestration platforms like Kubernetes. This study investigates the performance efficiency of container orchestration in multi-cloud environments by evaluating key parameters such as deployment time, resource utilization, latency, scalability, and fault tolerance. A comparative analysis is conducted using Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), and Azure Kubernetes Service (AKS), supported by statistica

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Chelliah, Pethuru Raj, and Chellammal Surianarayanan. "Multi-Cloud Adoption Challenges for the Cloud-Native Era." International Journal of Cloud Applications and Computing 11, no. 2 (2021): 67–96. http://dx.doi.org/10.4018/ijcac.2021040105.

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With the ready availability of appropriate technologies and tools for crafting hybrid clouds, the move towards employing multiple clouds for hosting and running various business workloads is garnering subtle attention. The concept of cloud-native computing is gaining prominence with the faster proliferation of microservices and containers. The faster stability and maturity of container orchestration platforms also greatly contribute towards the cloud-native era. This paper guarantees the following contributions: 1) It describes the key motivations for multi-cloud concept and implementations. 2

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Leiter, Ákos, Edina Lami, Attila Hegyi, József Varga, and László Bokor. "Closed-loop Orchestration for Cloud-native Mobile IPv6." Infocommunications journal 15, no. 1 (2023): 44–54. http://dx.doi.org/10.36244/icj.2023.1.5.

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With the advent of Network Function Virtualization (NFV) and Software-Defined Networking (SDN), every network service type faces significant challenges induced by novel requirements. Mobile IPv6, the well-known IETF standard for network-level mobility management, is not an exemption. Cloud-native Mobile IPv6 has acquired several new capabilities due to the technological advancements of NFV/SDN evolution. This paper presents how automatic failover and scaling can be envisioned in the context of cloud-native Mobile IPv6 with closed-loop orchestration on the top of the Open Network Automation Pla

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Anbarasu, Arivoli. "Container Orchestration at Scale: Comparing Kubernetes and Emerging Alternatives for Cloud-Native Applications." European Journal of Advances in Engineering and Technology 5, no. 11 (2018): 927–32. https://doi.org/10.5281/zenodo.15234680.

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Efficient container orchestration has become essential for scalability and reliability. Kubernetes remains the dominant orchestration platform, yet emerging alternatives offer unique advantages in performance, automation, and resource efficiency. Choosing the right solution requires balancing factors such as workload demands, fault tolerance, and operational overhead. Therefore, evaluating container orchestration platforms is critical for optimizing large-scale deployments. We propose a framework that helps organizations optimize scalability, automation, and resilience in container orchestrati

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Rajesh Kesavalalji. "Scalable and fault-tolerant microservices architecture: Leveraging AI-driven orchestration in distributed cloud systems." International Journal of Science and Research Archive 13, no. 1 (2024): 3501–11. https://doi.org/10.30574/ijsra.2024.13.1.1566.

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Artificial intelligence in microservices orchestration takes cloud computing to the next level, creating a fully automated, scalable, and efficient environment. Ai orchestration can optimize resource allocation along with increased fault tolerance in predictive analytics for large applications. This paper speaks about the contribution of AI in microservices orchestration, which has real-life applications like Netflix, Uber, and Amazon. Major differences between AI-enhanced microservices management and traditional microservices management include reliability in services through AI, performance,

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Mylsamy, Sekar, and Rupesh Kumar Mishra. "Cloud-Native Development and Deployment." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 4 (2025): 279–89. https://doi.org/10.63345/ijrmeet.org.v13.i4.17.

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Cloud-native development and deployment has revolutionized the modern software engineering landscape by embracing flexible, scalable, and resilient architectures. This approach leverages containerization, microservices, and orchestration frameworks to enable rapid iteration and continuous delivery. By decoupling application components, organizations achieve greater agility in responding to evolving market demands and technological advancements. Cloud-native methodologies prioritize the use of distributed systems that inherently scale, withstand failures, and facilitate seamless integration wit

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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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Katta, Tejaswi Bharadwaj. "AI-Enhanced Orchestration in Hybrid Cloud Enterprise Integration: Transforming Enterprise Data Flows." European Journal of Computer Science and Information Technology 13, no. 9 (2025): 92–103. https://doi.org/10.37745/ejcsit.2013/vol13n992103.

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Hybrid cloud enterprise integration presents a formidable challenge as organizations strive to harmonize legacy systems with modern, cloud-native applications. This article investigates the potential of AI-enhanced orchestration to dynamically manage integration workflows across such heterogeneous environments. By embedding artificial intelligence within orchestration platforms, enterprises can achieve real-time optimization of data flows, resource allocation, and security compliance, transforming static integration approaches into adaptive, self-healing systems. The article focuses on three k

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Researcher. "SPRING BOOT AND CLOUD-NATIVE ARCHITECTURES: BUILDING SCALABLE AND RESILIENT APPLICATIONS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 249–65. https://doi.org/10.5281/zenodo.13770951.

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This comprehensive article explores the synergy between Spring Boot and cloud-native architectures in creating scalable, resilient, and efficient applications. It delves into the key concepts of cloud-native development, including microservices, containerization, and orchestration, while highlighting Spring Boot's features that support these principles. The article presents statistical data on market growth, adoption rates, and performance improvements achieved by organizations implementing cloud-native architectures with Spring Boot. It also discusses best practices for building resilient app

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Sanjay Dahibhate, Makarand. "Enhancing Enterprise Data Orchestration Using Azure Data Factory." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03578.

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ABSTRACT This paper explores the principles and practices of data orchestration in the context of modern enterprise needs, emphasizing scalability, automation, and reliability. It provides a detailed overview of Azure Data Factory (ADF), Microsoft’s cloud-native orchestration tool, covering its architecture, key components, and operational workflows. Through a case study involving an e-commerce platform processing 50 GB of data daily, the study demonstrates ADF’s effectiveness in reducing execution time, minimizing errors, and enhancing developer productivity. Quantitative analysis highlights

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Ganore, Pramod. "Federated Learning in Cloud-Native Architectures: A Secure Approach to Decentralized AI." International Journal of Computing and Engineering 6, no. 8 (2024): 1–10. https://doi.org/10.47941/ijce.2762.

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Purpose: The paper aims to analyze the technical and security challenges of deploying FL at scale and explores how modern cloud-native technologies such as container orchestration, hybrid cloud infrastructure, and privacy-preserving techniques can be leveraged to mitigate these challenges. The study also seeks to provide a comprehensive understanding of how FL is being applied in critical domains such as healthcare, IoT, and cybersecurity, while identifying future trends that could shape the evolution of decentralized AI systems. Methodology: This research adopts a qualitative and architectura

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Vaño, Rafael, Ignacio Lacalle, Piotr Sowiński, Raúl S-Julián, and Carlos E. Palau. "Cloud-Native Workload Orchestration at the Edge: A Deployment Review and Future Directions." Sensors 23, no. 4 (2023): 2215. http://dx.doi.org/10.3390/s23042215.

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Cloud-native computing principles such as virtualization and orchestration are key to transferring to the promising paradigm of edge computing. Challenges of containerization, operative models and scarce availability of established tools make a thorough review indispensable. Therefore, the authors have described the practical methods and tools found in the literature as well as in current community-led development projects, and have thoroughly exposed the future directions of the field. Container virtualization and its orchestration through Kubernetes have dominated the cloud computing domain,

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Pakalapati, Maruti Pradeep. "Understanding Cloud-Native Architectures for Scalable Systems: A Comprehensive Analysis." International Journal of Computing and Engineering 7, no. 9 (2025): 68–79. https://doi.org/10.47941/ijce.2954.

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Cloud-native architectures have fundamentally changed how engineers build scalable, resilient distributed systems. This article tracks the gradual evolution away from monolithic applications toward more flexible microservices-based designs. Four essential principles emerge as defining characteristics: decomposition of services, container-based deployment, automated orchestration, and standardized API communication. Technical implementation details receive thorough attention, from the practical challenges of container runtime selection to the nuanced configuration of orchestration platforms and

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Sunil Sudhakaran. "Enabling Rapid Application Development through Reusable Cloud Process Orchestration and Workflow Automation Frameworks." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 06 (2025): 3002–9. https://doi.org/10.47392/irjaeh.2025.0442.

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Cloud-native applications increasingly depend on process orchestration and workflow automation frameworks to enable rapid, scalable, and resilient development practices. This review provides a comprehensive examination of reusable orchestration patterns and automation frameworks, highlighting their application in modern software delivery pipelines. We present a theoretical model for workflow composition and execution, supported by empirical benchmarks from tools like Zeebe, Argo, Apache Airflow, and AWS Step Functions. Key benefits include reduced development time, improved concurrency handlin

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Umamaheswarareddy Chintam. "Optimizing EAI with AI and Cloud-Native Platforms : A Comparative Study of Popular Integration Frameworks." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 405–15. https://doi.org/10.32628/cseit25112373.

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The integration of artificial intelligence with cloud-native platforms represents a transformative approach to Enterprise Application Integration, offering enhanced efficiency, scalability, and adaptability for modern businesses. This article examines the role of AI in optimizing EAI processes, with particular focus on its implementation across major cloud-native integration frameworks including SAP Cloud Platform Integration, MuleSoft, and Apache Camel. Through comparative analysis, this article evaluates how AI capabilities are embedded within these platforms to enhance data processing, work

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Ritesh Kumar Sinha. "Generative Migration Architectures: Accelerating Cloud-Native Data Integration Through AI Orchestration." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 709–18. https://doi.org/10.32996/jcsts.2025.7.5.79.

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The integration of artificial intelligence into cloud migration frameworks represents a paradigm shift in data engineering practices across enterprise ecosystems. Generative AI models embedded within migration toolchains demonstrate exceptional capability in predicting schema inconsistencies and autonomously resolving structural disparities between heterogeneous data sources. Serverless architectures leveraging event-driven processing create adaptable migration pipelines that dynamically scale with workload intensity, effectively eliminating traditional bottlenecks. The evolution toward AI-aug

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Molleti, Ramasankar. "Advancements in Blue-Green Deployment Techniques within Cloud Native Environments." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–7. https://doi.org/10.55041/ijsrem17005.

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Blue-green deployment has become very important in ensuring that there is minimal time when the system is offline and reducing other risks associated with updating the software in cloud-native architecture. This paper aims to discuss the modern trends in blue-green deployment, focusing on the change from simple to complex structures. In this paper, the discussion on Containerization, orchestration platforms, and automated testing pipelines has greatly improved the capability of blue-green deployment. Some of the issues that are covered include the issue of managing database schema, managing th

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Pentyala, Dillep kumar. "Enhancing Data Reliability in Cloud-Native Environments through AI-Orchestrated Processes." Research and Analysis Journal 4, no. 12 (2021): 22–35. https://doi.org/10.18535/raj.v4i12.271.

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In today’s fast-evolving digital landscape, cloud-native environments have emerged as the cornerstone of scalable and flexible computing. However, ensuring data reliability within these environments remains a critical challenge due to the dynamic nature of cloud infrastructure, resource variability, and the increased frequency of system failures. Traditional data reliability mechanisms, such as redundancy and replication, often fall short in addressing the complex demands of modern cloud-native applications. This paper proposes an innovative approach to enhancing data reliability through the i

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Sai Manish Podduturi. "Cloud-Native Microservices for Real-Time Data Systems: A Technical Deep Dive." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 907–17. http://dx.doi.org/10.32628/cseit241061142.

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Cloud-native microservices have emerged as a transformative solution for organizations facing the challenges of real-time data processing and analysis in today's digital landscape. This comprehensive article explores the fundamental components of cloud-native architectures, from their foundational principles to implementation best practices. The article examines how containerization, orchestration, and event-driven architectures enable unprecedented scalability and efficiency in handling modern data processing requirements. Through a detailed examination of microservices as building blocks, th

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Rajani Jayantha. "Serverless evolution: A technical review of cloud-native development future." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 2325–33. https://doi.org/10.30574/wjaets.2025.15.3.1055.

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Serverless computing has undergone a remarkable transformation from experimental technology to enterprise-grade infrastructure foundation, fundamentally reshaping cloud-native application development paradigms. This comprehensive technical review explores the evolutionary trajectory of serverless technologies, examining market maturation patterns, enterprise adoption strategies, and technological innovations that have established serverless computing as a critical component of modern cloud architectures. The serverless ecosystem has experienced substantial growth across global infrastructure d

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Srikanth, Kandragula. "Cloud-native Application Development and Deployment." European Journal of Advances in Engineering and Technology 8, no. 12 (2021): 152–54. https://doi.org/10.5281/zenodo.14006008.

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The ever-evolving landscape of software development has witnessed the emergence of cloud computing as a dominant force. Cloud-native application development and deployment represents a revolutionary approach specifically designed to create and operate software applications that flourish within the cloud environment. Unlike traditional methods of simply porting applications to the cloud, cloud-native applications are meticulously architected to capitalize on the inherent scalability, flexibility, and automation capabilities offered by cloud platforms. This paper delves into the core principles

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Omoniyi David Olufemi. "AI-enhanced predictive maintenance systems for critical infrastructure: Cloud-native architectures approach." World Journal of Advanced Engineering Technology and Sciences 13, no. 2 (2024): 229–57. http://dx.doi.org/10.30574/wjaets.2024.13.2.0552.

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Critical infrastructure (CI), such as power grids, transportation systems, and telecommunications networks, is becoming increasingly complex, requiring sophisticated maintenance strategies and procedures to guarantee optimal performance and system durability. This paper examines the transformational potential of AI-driven predictive maintenance systems, highlighting their ability to prevent system failures, minimize downtime, and enhance resource efficiency. Integrating machine learning algorithms with real-time data analytics allows predictive maintenance frameworks to accurately foresee equi

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Researcher. "LEVERAGING CLOUD-NATIVE ARCHITECTURE FOR SCALABLE AND RESILIENT ENTERPRISE APPLICATIONS: A COMPREHENSIVE ANALYSIS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 583–91. https://doi.org/10.5281/zenodo.13861921.

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This comprehensive article explores the transformative impact of cloud-native architectures on enterprise applications, focusing on their ability to enhance scalability, resilience, and agility in today's rapidly evolving digital landscape. The article begins by elucidating cloud-native design's core principles and key components, including microservices, containers, and dynamic orchestration technologies like Kubernetes. It then delves into the strategies for achieving scalability and resilience in cloud-native systems, supported by real-world case studies from industry leaders. The arti

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Prem Nishanth Kothandarama. "Designing Developer Platforms for Cross-Cloud Portability and Scale." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 06 (2025): 3017–23. https://doi.org/10.47392/irjaeh.2025.0444.

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The recent ramp-up in multi-cloud strategy deployment has forced a redesign of the developer platform in terms of portability and scalability across the heterogeneous cloud world. In this review, we explore architectural and operational considerations for the deployment of developer platforms that run without compromise across multiple cloud providers. It provides a multidimensional view of a framework that includes architectural abstraction, developer experience, data management, performance engineering and security integration. Container orchestration, infrastructure as code and service mesh

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Aravind Nuthalapati. "Scaling AI Applications on the Cloud toward Optimized Cloud-Native Architectures, Model Efficiency, and Workload Distribution." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 2 (2025): 200–206. https://doi.org/10.51583/ijltemas.2025.14020022.

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Abstract: The rapid growth of Artificial Intelligence (AI) has increasefd the demand for scalable, efficient, and cost-effective computational infrastructure. Traditional on-premise systems face limitations in scalability, resource allocation, and cost efficiency, making cloud computing a preferred solution. This paper examines cloud-native architectures, including containerization, Kubernetes orchestration, serverless computing, and microservices, as key enablers of AI scalability. Modern approaches for optimizing AI models involve using quantization and pruning and knowledge distillation app

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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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Abhyudaya Gurram. "Modernizing legacy enterprise platforms: A cloud-native migration case study." Global Journal of Engineering and Technology Advances 23, no. 1 (2025): 376–89. https://doi.org/10.30574/gjeta.2025.23.1.0125.

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A large financial services enterprise undertook a transformative cloud-native migration journey to modernize its legacy platform. The initiative encompassed comprehensive assessment phases, strategic planning, and systematic implementation across infrastructure, applications, and security domains. The migration successfully addressed critical challenges including scalability limitations, technical debt, and operational inefficiencies. Through careful orchestration of cloud technologies, microservices architecture, and DevOps practices, the organization achieved substantial improvements in depl

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Adusumilli, Lakshmi Vara Prasad. "Serverless Kubernetes: The Evolution of Container Orchestration." European Journal of Computer Science and Information Technology 13, no. 30 (2025): 20–36. https://doi.org/10.37745/ejcsit.2013/vol13n302036.

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This article examines the convergence of serverless computing and Kubernetes orchestration, representing a significant advancement in cloud-native architecture. Serverless Kubernetes implementations address fundamental operational challenges of traditional container orchestration while preserving its powerful capabilities. It explores the technical foundations enabling this evolution, including Virtual Kubelet for node abstraction, KEDA for event-driven scaling, and Knative for serverless abstractions. It analyzes implementations from major cloud providers—AWS EKS on Fargate, Azure Container I

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Anishkumar, Sargunakumar. "Container Orchestration: Key Challenges and Opportunities with Kubernetes." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 5 (2024): 1–9. https://doi.org/10.5281/zenodo.15086842.

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Container orchestration has become a fundamental aspect of modern cloud-native applications, enabling efficient automation, scaling, and management of containerized workloads. Kubernetes, as the leading open-source orchestration platform, provides robust features that facilitate container deployment and lifecycle management. However, it also introduces several complexities, including security concerns, networking challenges, and resource optimization issues. This paper explores these key challenges and identifies opportunities for automation, AI-driven optimizations, and integration with emerg

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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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Vissarapu, Srikanth. "Generative AI in Cloud-Native Development: Automating Code, Configs, and Deployment." European Journal of Computer Science and Information Technology 13, no. 38 (2025): 145–56. https://doi.org/10.37745/ejcsit.2013/vol13n38145156.

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Generative AI is transforming cloud-native development through sophisticated automation capabilities across the software engineering lifecycle. By leveraging large language models and AI-powered tools, organizations can accelerate infrastructure provisioning, optimize application configurations, and enhance deployment reliability. This article explores how AI technologies are revolutionizing code generation, configuration management, and deployment orchestration in cloud environments. The integration of natural language processing, code understanding, and pattern recognition capabilities enabl

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Akinniyi James Samuel. "Cloud-Native AI solutions for predictive maintenance in the energy sector: A security perspective." World Journal of Advanced Research and Reviews 9, no. 3 (2021): 409–28. https://doi.org/10.30574/wjarr.2021.9.3.0052.

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The integration of cloud-native artificial intelligence (AI) technologies into predictive maintenance frameworks within the energy sector has emerged as a pivotal paradigm for enhancing operational reliability, optimizing asset performance, and minimizing unplanned downtime. This paper presents a comprehensive analysis of cloud-native AI solutions specifically tailored for predictive maintenance, emphasizing the inherent security implications associated with deploying such architectures in mission-critical energy infrastructures. Through an in-depth exploration of containerized microservices,

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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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Harsh, Gupta, and Hussain Arshad. "Role of DevOps in Modern Cloud-Native Application." Career Point International Journal of Research(CPIJR) 1, no. 4 (2025): 46–52. https://doi.org/10.5281/zenodo.15137129.

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The rise of cloud-native applications has transformed software development, with DevOps playing a critical role in enabling faster, more efficient, and reliable deployment of applications. This paper explores the role of DevOps in cloud-native environments, where automation, continuous integration, and continuous delivery (CI/CD) are essential for managing complex, distributed applications at scale. DevOps practices such as Infrastructure as Code (IaC), automated testing, and container orchestration streamline the development process, allowing teams to deploy applications across multi-cloud or

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Jagadeesh Kola. "Automation Frameworks and Best Practices for Cloud-Native Database Lifecycle Management." International Journal of Scientific Research in Science and Technology 11, no. 4 (2024): 675–86. https://doi.org/10.32628/ijsrst2415412.

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The migration to cloud-native frameworks makes it imperative to effectively manage database lifecycle across organizations. Also, well developed and automated applications and web systems are needed for effectiveness and scalability of database that meets global standard. This study attempts a comprehensive review of best practices and automation frameworks for effective management of cloud-native database lifecycle. These include scaling, decommissioning, configuration, monitoring and provisioning. The study analyzes orchestration of container and the place of infrastructure as Code (IaC) in

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Ramadass, Rosh Perumpully. "Enterprise Kubernetes Management: A GitOps-Driven Approach to Multi-Cluster Orchestration." European Journal of Computer Science and Information Technology 13, no. 47 (2025): 50–60. https://doi.org/10.37745/ejcsit.2013/vol13n475060.

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Enterprise Kubernetes adoption has revolutionized cloud-native infrastructure management, driving organizations toward centralized control systems. The increasing complexity of distributed cluster management has led to the development of sophisticated platforms that leverage GitOps principles for orchestration. These platforms address critical challenges in configuration management, security compliance, and operational efficiency through automated workflows and standardized practices. The implementation of centralized management solutions has enabled organizations to achieve enhanced security

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Sai Teja Battula. "A Comprehensive Framework for Evaluating the Scalability and Security of Fintech Web Applications in a Cloud-Native Environment." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1940–50. https://doi.org/10.32628/cseit23112567.

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This article presents a comprehensive framework for evaluating and designing cloud-native fintech applications that balance scalability requirements with robust security postures. As financial institutions increasingly migrate to cloud environments, they face complex challenges at the intersection of technological innovation, regulatory compliance, and cybersecurity. The article examines core architectural considerations, including scalability patterns like microservices and event-driven design, alongside essential security frameworks such as zero-trust architecture and defense-in-depth strate

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Tejasvi Nuthalapati. "How the cloud connects everything: Demystifying enterprise system integration." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1584–94. https://doi.org/10.30574/wjaets.2025.15.2.0696.

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Cloud-native integration has fundamentally transformed enterprise system architecture, breaking down traditional silos that have long plagued organizations. This comprehensive article examines how modern cloud technologies enable seamless connections between previously isolated systems—from HR platforms to finance tools and marketing systems. By leveraging APIs as universal connectors, event-driven architectures for real-time responsiveness, and sophisticated service orchestration for complex workflows, enterprises can create cohesive digital ecosystems that adapt to changing business needs. T

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Mahitha Adapa. "Dynamic Resource Management in Healthcare: A Case Study of Kubernetes-Based Platform Scaling." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1064–78. https://doi.org/10.32628/cseit251112111.

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This article presents a comprehensive analysis of a large-scale Kubernetes-based healthcare platform implementation, demonstrating the transformative potential of cloud-native architecture in healthcare operations. The article examines the design, implementation, and operational outcomes of a dynamically scalable infrastructure serving millions of patients while maintaining strict regulatory compliance. Through detailed analysis of the platform's architecture, monitoring systems, and resource management mechanisms, this article provides insights into achieving operational excellence in healthc

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Nishanth Reddy Pinnapareddy. "Cloud Cost Optimization and Sustainability in Kubernetes." Journal of Information Systems Engineering and Management 10, no. 45s (2025): 533–51. https://doi.org/10.52783/jisem.v10i45s.8895.

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The examination investigates how cloud cost optimization must fit dual requirements of environmental sustainability when applied to Kubernetes-based deployments, as these serve as crucial elements in modern cloud-native environments. Due to its remarkable operational features, Kubernetes became the leading container orchestration system after Google initially developed it, as it provides flexibility and resilience alongside scalability. Resource management proves challenging within Kubernetes deployments owing to their properties, which lead to high cloud costs and adverse environmental outcom

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Khushmeet Singh. "Performance Optimization and Cost Control in Snowflake: A Strategic Approach." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 05 (2025): 1623–29. https://doi.org/10.47392/irjaem.2025.0262.

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As organizations increasingly migrate critical data workloads to cloud-native platforms, Snowflake has emerged as a leading data warehouse solution offering flexibility, scalability, and performance. However, its utility-based pricing model introduces new complexities in managing cost and optimizing performance. This review provides a strategic analysis of Snowflake’s architectural elements, AI-driven optimization approaches, cost governance techniques, and workload management best practices. Experimental results demonstrate that intelligent orchestration, auto-scaling, and query optimization

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Chandrasehar, Amreth. "ML Powered Container Management Platform: Revolutionizing Digital Transformation through Containers and Observability." Journal of Artificial Intelligence & Cloud Computing 1, no. 1 (2022): 1–3. http://dx.doi.org/10.47363/jaicc/2023(1)130.

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As companies adopt digital transformation, cloud-native applications become a critical part of their architecture and roadmap. Enterprise applications and tools are developed using cloud native architecture are containerized and are deployed on container orchestration platforms. Containers have revolutionized application deployments to help management, scaling and operations of workloads deployed on container platforms. But a lot of issues are faced by operators of the platforms such as complexity in managing large scale environments, security, networking, storage, observability and cost. This

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Ajay Averineni. "Optimizing cloud-native microservices for scalability and cost efficiency." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1461–75. https://doi.org/10.30574/wjaets.2025.15.2.0679.

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This technical article explores the transformative shift from monolithic architectures to cloud-native microservices, highlighting the fundamental advantages in scalability, cost efficiency, and agility. The article explores key components that enable successful microservices implementations, including containerization with Docker, orchestration with Kubernetes, and managed cloud services. It delves into essential design considerations for scalability through service decomposition, stateless design principles, and effective auto-scaling strategies. Cost optimization techniques are thoroughly a

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BuchiReddy Karri, Sairamakrishna, Chandra Mouli Penugonda, Srujana Karanam, Mohd Tajammul, Srinivasarao Rayankula, and Prasad Vankadara. "Enhancing Cloud-Native Applications: A Comparative Study of Java-To-Go Micro Services Migration." International Transactions on Electrical Engineering and Computer Science 4, no. 1 (2025): 1–12. https://doi.org/10.62760/iteecs.4.1.2025.127.

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Moving microservices from Java to Go creates great opportunities for performance, scalability, and resource efficiency. Nonetheless, such a move comes with other challenges related to infrastructure changes, deployment strategies, observability, and security. This paper tries to look at elements of paramount importance as concerned with Java-to-Go migration, thereby, interrogating the key hosting environments, containerization, and orchestration. Go as a light engine introduces one of the most cost-effective deployments as organizations lean towards cloud-native architectures and Kubernetes-ba

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Researcher. "BUILDING SCALABLE CLOUD-NATIVE APPLICATIONS WITH SPRING BOOT AND KUBERNETES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 300–310. https://doi.org/10.5281/zenodo.14129813.

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This comprehensive article explores the evolution and implementation of cloud-native applications, focusing on their transformative impact on modern software development. The article examines key statistics showing 147% growth in cloud-native adoption between 2019-2021, with 6.5 million developers worldwide embracing these technologies. Through a detailed examination of microservices architecture, Spring Boot framework, and Kubernetes orchestration, the analysis reveals significant improvements, including 50% faster deployment frequencies, 70% reduction in failure rates, and 99.99% system avai

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Aelken, Jörg, Joan Triay, Bruno Chatras, and Arturo Martin de Nicolas. "Toward Cloud-Native VNFs: An ETSI NFV Management and Orchestration Standards Approach." IEEE Communications Standards Magazine 8, no. 2 (2024): 12–19. http://dx.doi.org/10.1109/mcomstd.0002.2200079.

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Preetham Kumar Dammalapati. "Advances in cloud-native microservices for system integration." World Journal of Advanced Research and Reviews 26, no. 3 (2025): 196–206. https://doi.org/10.30574/wjarr.2025.26.3.2165.

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Cloud-native microservices have revolutionized system integration strategies across enterprise architectures, offering unprecedented advantages in scalability, resilience, and agility. This architectural paradigm decomposes monolithic applications into independently deployable, loosely coupled services that can be developed and managed separately. The transition to microservices enables more efficient development cycles through container-based deployments while allowing teams to work autonomously on distinct components. Modern integration patterns, including API-first design, event-driven comm

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Sharma, Vivek. "AI-DRIVEN CLOUD INFRASTRUCTURE: ADVANCES IN KUBERNETES AND SERVERLESS COMPUTING." international journal of advanced research in computer science 16, no. 2 (2025): 65–70. https://doi.org/10.26483/ijarcs.v16i2.7234.

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Artificial Intelligence has been integrated into cloud infrastructure, making it revolutionizing modern computing by automating, scaling, and efficiency. The first of these is Kubernetes and the second is serverless computing. Kubernetes, a container orchestration platform, benefits from AI-driven enhancements in workload scheduling, auto-scaling, and resource optimization. By combining AI based predictive analytics with container deployment, overhead is reduced in terms of the operational overhead as well as the fault tolerance. However, serverless computing takes away the management of infra

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Srinivasa, Rao Karanam. "The Evolution of Data Warehousing: From On-Premise to Cloud-Native Solutions." Journal of Advances in Developmental Research 15, no. 2 (2024): 1–9. https://doi.org/10.5281/zenodo.15206387.

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Throughout the broader timeline of enterprise computing, data warehousing has become an integral approach for consolidating disparate data sets into the centralized, structured repository. Initial on-premise models emphasized intricately planned schema designs and hardware provisioning, but with the advent of highly scalable Cloud infrastructures, the complexities of deployment and management began to shift drastically. This paper evaluates the transitions from historical on-premises architecture, which demanded massive capital outlays, into more flexible cloud-based data warehouse topologies

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Liu, Peng, Jinsong Wang, Weisen Zhao, and Xiangjun Li. "Research and Implementation of Container Based Application Orchestration Service Technology." Journal of Physics: Conference Series 2732, no. 1 (2024): 012012. http://dx.doi.org/10.1088/1742-6596/2732/1/012012.

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Abstract With the rapid development of cloud computing technology, Kubernetes(K8S), as the main orchestration tool for cloud native applications, has become the preferred choice for enterprises and developers. This article is based on container based application orchestration service technology. Through a set of templates containing cloud resource descriptions, it quickly completes functions such as application creation and configuration, application batch cloning, and application multi environment deployment. It simplifies and automates the lifecycle management capabilities required for cloud

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