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6 articles for “Kubernetes”
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Managing and Maintaining Container inside Kubernetes Clusters
Abstract: It is an open-source orchestration tool developed by Google, its historical name is bought after it has been changed to Kubernetes, now it is part of the Cloud Native Computing Foundation. Kubernetes is mostly used for maintaining micro servers and containers. Here, container refers to an existing application that has been containerized using Docker and can only be implemented within Kubernetes. Micro servers are used when there are multiple little …
Published in Recent Trends in Parallel Computing · Vol. 9, Issue 2, 2022 · pp. 6–12 Read article
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Efficient Design and Optimization of Cloud-Native Backend Services for Full-Stack Web Applications
Abstract: The fast development of cloud computing has immensely altered architectural outlays of the current full stack web applications. Conventional monolithic back-end systems tend to have a lack of scalability, complex deployment, and wasted resources. To overcome these issues, this paper will design and optimize cloud-native Java based back-end microservices based on Spring Boot, Docker, Kubernetes and REST based models of communication. This architecture proposal has containerized microservice implementation with an …
Published in Journal of Web Engineering & Technology · Vol. 13, Issue 2, 2026 · pp. 24–32 Read article
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A Study of Open-source Cloud Computing Platforms
Abstract: Cloud computing has modernized the way organizations manage, store, and process data, offering scalable and cost-effective solutions. Open-source cloud computing platforms have emerged as a key enabler in this transformation, providing flexibility, transparency, and community-driven innovation. This study discusses the most prominent open-source cloud platforms, including OpenStack, CloudStack, and Kubernetes, highlighting their architecture, features, and use cases. The study deals with their comparative strengths, limitations, and adoption challenges in various …
Published in Journal of Open Source Developments · Vol. 12, Issue 1, 2025 · pp. 25–36 Read article
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Machine Learning Pipelines: A Survey on Automation, Scalability, and Deployment Strategies
Abstract: Machine learning (ML) has become a critical enabler of intelligent applications across domains, requiring robust, efficient, and scalable deployment workflows. This review paper provides an in-depth overview of machine learning pipelines, emphasizing three key dimensions: automation, scalability, and deployment methodologies. It begins by exploring automation techniques that reduce manual effort in data ingestion, preprocessing, model selection, and hyperparameter tuning. Tools such as AutoML, TFX, and workflow orchestration platforms are examined …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 17–28 Read article
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AI-Driven DevSecOps Automation: An Intelligent Framework for Continuous Cloud Security and Regulatory Compliance
Abstract: Cloud-native systems, microservices, and infrastructure-as-code (IaC)–oriented CI/CD pipelines have accelerated the pace of software delivery, yet they have also introduced new layers of operational complexity and widened the overall security exposure of modern applications. Traditional DevSecOps workflows still depend heavily on isolated scanners, manual reviews, and static governance processes that are not well-suited for the elasticity and constant change characteristic of multi-cloud environments. To address these limitations, this paper introduces …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 · pp. 01–15 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article