Relevant bibliographies by topics / Orchestration cloud native

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Orchestration cloud native'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Orchestration cloud native"

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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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Dissertations / Theses on the topic "Orchestration cloud native"

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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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Dridi, Mohamed Amine. "Platform-based 5G service design and orchestration." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAS002.

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A partir de la cinquième génération, les réseaux mobiles devront supporter une croissance exponentielle du nombre d'appareils connectés de différents types, ceci étant l'un des piliers d'une stratégie globale de numérisation accélérée. En plus de cette croissance de connectivité, ces réseaux devront également supporter et offrir divers services pour de nouvelles industries aux exigences hétérogènes. Les concepteurs et développeurs de réseaux 5G sont alors contraints de fournir de nouvelles solutions et d'optimiser celles qui existent pour contenir les demandes croissantes de bande passante et
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Noroozi, Hamid. "A Cloud-native Vehicular Public Key Infrastructure : Towards a Highly-available and Dynamically- scalable VPKIaaS." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300658.

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Efforts towards standardization of Vehicular Communication Systems (VCSs) have been conclusive on the use of Vehicular Public-Key Infrastructure (VPKI) for the establishment of trust among network participants. Employing VPKI in Vehicular Communication (VC) guarantees the integrity and authenticity of Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs). It also offers a level of privacy for vehicles as VPKI provides them with a set of non-linkable short-lived certificates, called pseudonyms, which are used to sign outgoing messages by vehicles wh
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Books on the topic "Orchestration cloud native"

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Gutierrez, Felipe. Spring Cloud Data Flow: Native Cloud Orchestration Services for Microservice Applications on Modern Runtimes. Apress, 2019.

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Ruecker, Bernd. Practical Process Automation: Orchestration and Integration in Microservices and Cloud Native Architectures. O'Reilly Media, 2021.

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Book chapters on the topic "Orchestration cloud native"

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Quenum, José Ghislain, and Gervasius Ishuuwa. "Abstracting Containerisation and Orchestration for Cloud-Native Applications." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59635-4_12.

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Christian, Juan, Luis Paulino, and Alan Oliveira de Sá. "A Low-Cost and Cloud Native Solution for Security Orchestration, Automation, and Response." In Information Security Practice and Experience. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21280-2_7.

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Dias, Tomás, Luís Ferreira, Diogo Fevereiro, Luis Rosa, Luis Cordeiro, and João Fernandes. "Cloud-Native Scheduling and Resource Orchestration: A Deep Dive into AI-Driven Approaches." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-97317-8_8.

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Janjua, Hafiza Kanwal, Ignacio de Miguel, Ramón J. Durán Barroso, et al. "A Framework for Next Generation Cloud-Native SDN Cognitive Resource Orchestrator for IoTs (NG2CRO)." In Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38318-2_39.

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Pusapati, Sai Samin Varma. "Containerization." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1682-5.ch006.

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This chapter delves into the synergy between containerization and serverless computing, pivotal for advancing cloud-native application deployment. It outlines the architectural foundations and benefits of each paradigm, emphasizing their combined impact on scalability, efficiency, and agility. The discussion progresses to technical integrations, focusing on container orchestration and serverless platforms, enhancing management and deployment. Addressing challenges like security and operational complexity, it highlights strategies for navigating these issues. Real-world examples illustrate the
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Lakkarasu, Phanish. "Best practices in building secure, compliant, and resilient cloud-native architectures for artificial intelligence workloads." In Designing Scalable and Intelligent Cloud Architectures: An End-to-End Guide to AI Driven Platforms, MLOps Pipelines, and Data Engineering for Digital Transformation. Deep Science Publishing, 2025. https://doi.org/10.70593/978-93-49910-08-9_4.

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Cloud-native architectures are characterized as a strategy for building and running applications that exploits the advantages of the cloud computing delivery model. Using cloud-native technologies, developers and organizations can build and run scalable applications in an environment that overcomes the constraints of a traditional data center and of proprietary technology stacks. Today, we can give even stronger definitions and more accurate terms to characterize these architectures, with even increased levels of common agreement. In particular, we posit that cloud-native architectures are dis
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Lakkarasu, Phanish. "Leveraging cloud-native microservices, containers, and serverless architectures for artificial intelligence pipelines." In Designing Scalable and Intelligent Cloud Architectures: An End-to-End Guide to AI Driven Platforms, MLOps Pipelines, and Data Engineering for Digital Transformation. Deep Science Publishing, 2025. https://doi.org/10.70593/978-93-49910-08-9_11.

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AI pipeline automation can be described as orchestrating the machine learning or deep learning life cycle with a software suite. These software suites could be open-source popular tools such as Kube Flow or MLflow, commercial services by platform providers such as AWS SageMaker and Azure ML, or something between which is a solution built on top of orchestration tools such as Apache Airflow or Google Cloud Workflows. These AI pipelines can provide benefits such as organized code, flexible workflows, and easy reproducibility. In machine learning, being able to reproduce your work (and someone do
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Soussane, Khalil, Bahaa Eddine Elbaghazaoui, and Mohamed Amnai. "Integrating Security in DataOps." In Data Governance, DevSecOps, and Advancements in Modern Software. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0365-9.ch010.

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DataOps is crucial for managing complex data pipelines and driving efficient data analytics, but it introduces significant security challenges as data flows through ingestion, transformation, storage, and distribution stages. Each phase exposes unique vulnerabilities that threaten data integrity, confidentiality, and compliance. This chapter outlines a structured framework for embedding security into DataOps workflows, focusing on methods like encryption, access control, and real-time monitoring to safeguard data without disrupting operations. Leveraging advanced tools, including Security Orch
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Conference papers on the topic "Orchestration cloud native"

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Pham, Van-Quan, Ahan Kak, Huu-Trung Thieu, and Nakjung Choi. "ORCA: Cloud-native Orchestration and Automation of E2E Cellular Network Functions and Slices." In IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2024. http://dx.doi.org/10.1109/infocomwkshps61880.2024.10620766.

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Baranda, Jorge, Javier Velázquez Martínez, Albert Bel, David Gregoratti, Luis M. Contreras, and Josep Mangues-Bafalluy. "Full Orchestration of a Distributed 5G Cloud-Native Mobile Network: O-Ran, Core, and Transport." In 2025 IEEE 11th International Conference on Network Softwarization (NetSoft). IEEE, 2025. https://doi.org/10.1109/netsoft64993.2025.11080589.

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Tzanettis, Ioannis, Grigorios Kakkavas, Alexandros Stylos, Anastasios Zafeiropoulos, and Symeon Papavassiliou. "A Cloud-Native Approach for Orchestrating 6G-Enabled Services at the Computing Continuum." In 2024 IEEE 29th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). IEEE, 2024. https://doi.org/10.1109/camad62243.2024.10942907.

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LUONG, Duc-Hung, Huu-Trung THIEU, Abdelkader OUTTAGARTS, and Yacine GHAMRI-DOUDANE. "Predictive Autoscaling Orchestration for Cloud-native Telecom Microservices." In 2018 IEEE 5G World Forum (5GWF). IEEE, 2018. http://dx.doi.org/10.1109/5gwf.2018.8516950.

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Di Stefano, Alessandro, Antonella Di Stefano, and Giovanni Morana. "Ananke: A framework for Cloud-Native Applications smart orchestration." In 2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). IEEE, 2020. http://dx.doi.org/10.1109/wetice49692.2020.00024.

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Chowdhury, Rasel, Chamseddine Talhi, Hakima Ould-Slimane, and Azzam Mourad. "A Framework for Automated Monitoring and Orchestration of Cloud-Native applications." In 2020 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2020. http://dx.doi.org/10.1109/isncc49221.2020.9297238.

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Barrachina-Munoz, Sergio, Jorge Baranda, Miquel Payaro, and Josep Mangues-Bafalluy. "Intent-Based Orchestration for Application Relocation in a 5G Cloud-native Platform." In 2022 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2022. http://dx.doi.org/10.1109/nfv-sdn56302.2022.9974703.

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Di Stefano, Alessandro, Antonella Di Stefano, Giovanni Morana, and Daniele Zito. "Prometheus and AIOps for the orchestration of Cloud-native applications in Ananke." In 2021 IEEE 30th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). IEEE, 2021. http://dx.doi.org/10.1109/wetice53228.2021.00017.

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Grings, Felipe Hauschild, Lucas Baleeiro Dominato Silveira, Kleber Vieira Cardoso, Sand Correa, Lucio Rene Prade, and Cristiano Bonato Both. "Full dynamic orchestration in 5G core network slicing over a cloud-native platform." In GLOBECOM 2022 - 2022 IEEE Global Communications Conference. IEEE, 2022. http://dx.doi.org/10.1109/globecom48099.2022.10000663.

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Joshi, Suyash, Basit Hasan, and R. Brindha. "Optimal Declarative Orchestration of Full Lifecycle of Machine Learning Models for Cloud Native." In 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2024. http://dx.doi.org/10.1109/icaaic60222.2024.10575306.

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