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Littérature scientifique sur le sujet « Cloud Infrastructure Optimization »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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Articles de revues sur le sujet "Cloud Infrastructure Optimization"

Sairohith, Thummarakoti, Prasad Udayagiri, and Harikrishna Reddy B. "Adaptive Orchestration for Performance Cost Optimization in Multi-Cloud Infrastructure." European Journal of Advances in Engineering and Technology 7, no. 8 (2020): 119–25. https://doi.org/10.5281/zenodo.15307951.

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Organizations can benefit from using multiple cloud systems through multi-cloud infrastructure but must navigate high platform management difficulties. This document presents an automatic workflow management system to shift workloads among clouds while continuously optimizing performance and expense levels. Real-time performance tracking allows decision-makers to maximize provider workflow distribution according to their costs. The system utilizes an orchestration engine to link monitoring with decision-making processes and automatic provisioning, ensuring efficient operation among various clo

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Lakhwani, Kamlesh, Gajanand Sharma, Ramandeep Sandhu, et al. "Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment." International Journal of Cloud Applications and Computing 13, no. 1 (2023): 1–25. http://dx.doi.org/10.4018/ijcac.324809.

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Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient when virtual machines and other resources work up to their full potential. The main factor that influences the quality of cloud services is the distribution of workflow on virtual machines (VMs). Scheduling tasks to VMs depends on the type of workflow and mechanism of resource allocation. Scientific workflows include large-scale data transfer and consume intensive resources of cloud infrastructures. Therefore, s

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Nida, Bhanu Raju. "The Rise of Serverless Computing: Towards a Future Without Infrastructure Management." International Scientific Journal of Engineering and Management 03, no. 10 (2024): 1–7. https://doi.org/10.55041/isjem02093.

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Abstract—Emerging as a radical paradigm in cloud computing, serverless computing allows developers to deploy and run programs free from control of underlying infrastructure. The basic ideas of serverless computing, its main traits, and its rising relevance in contemporary cloud computing are investigated in this work. We evaluate the advantages and drawbacks of serverless computing including cost effectiveness, scalability, and operational simplicity by means of an analysis of market trends, technology developments, and practical case studies. We also look at difficulties such vendor lock-in,

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Ramamohan Kummara. "AI-Driven Cloud Optimization : Transforming Modern Infrastructure Management." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1152–69. https://doi.org/10.32628/cseit25112447.

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This article explores how AI-driven cloud optimization is transforming modern infrastructure management by enabling organizations to maximize their cloud investments while maintaining optimal performance. The convergence of artificial intelligence, machine learning, and cloud computing technologies has created systems capable of analyzing operational patterns, predicting resource requirements, and automatically adjusting cloud configurations without human intervention. It examines five key benefits of AI-driven optimization: cost reduction through intelligent resource allocation, performance e

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Banditwattanawong, Thepparit, Masawee Masdisornchote, and Putchong Uthayopas. "Multi-provider cloud computing network infrastructure optimization." Future Generation Computer Systems 55 (February 2016): 116–28. http://dx.doi.org/10.1016/j.future.2015.09.002.

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Sapana, Garud. "Optimizing Cloud Infrastructure through Advanced Development Practices in Financial Applications." Applied Science and Engineering Journal for Advanced Research 4, no. 1 (2025): 50–55. https://doi.org/10.5281/zenodo.14868988.

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Scalability, cost-effectiveness, and security are all advantages of cloud computing, which has become an indispensable component of financial applications. The optimization of infrastructure to enhance performance, reduce expenses, and enhance security remains a challenge as financial institutions continue to transition to the cloud. This study examines the impact of advanced development methodologies—DevOps, microservices architecture, Infrastructure as Code (IaC), and cloud cost optimization techniques—on the efficacy of cloud services in financial applications. The research eval

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Piyush Dhar Diwan. "GenAI-driven cloud management for AWS and Kubernetes environments." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 1475–84. https://doi.org/10.30574/wjarr.2025.26.1.1165.

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Generative Artificial Intelligence (GenAI) is transforming cloud platform engineering, bringing unprecedented intelligence and automation to infrastructure management for AWS and Kubernetes environments. This article examines how GenAI technologies like Amazon Q, Q-Developer, KubeGPT, and k8sGPT are revolutionizing the entire cloud infrastructure lifecycle—from design and provisioning to operations and optimization. These tools leverage natural language processing and machine learning to simplify complex tasks, translate technical concepts into actionable insights, and enable proactive managem

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Vijayakumar Jayaseelan. "Automated cost optimization for cloud infrastructure with generative AI: A technical deep dive." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 812–21. https://doi.org/10.30574/wjaets.2025.15.1.0292.

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Automated Cost Optimization for Cloud Infrastructure powered by Generative AI represents a transformative approach to managing and optimizing cloud expenses. This comprehensive article examines how artificial intelligence and machine learning technologies are revolutionizing cloud cost management through automated analysis, prediction, and optimization. The article investigates the challenges organizations face in cloud cost management and demonstrates how AI-driven solutions provide enhanced visibility, improved resource utilization, and automated optimization capabilities. Through analysis o

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Shanmugavadivelu, Priyadarshni. "Powering Health Care in the Cloud: How VM Optimization Is Modernizing Healthcare Infrastructure." European Journal of Computer Science and Information Technology 13, no. 39 (2025): 152–62. https://doi.org/10.37745/ejcsit.2013/vol13n39152162.

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The transition of healthcare organizations to cloud computing represents a transformative shift in information technology infrastructure management. This comprehensive article examines how virtual machine optimization is revolutionizing healthcare delivery through specialized cloud architectures. The migration from traditional on-premises systems to cloud-based Electronic Health Record platforms delivers substantial advantages in scalability, cost efficiency, and performance. Healthcare workloads present unique challenges requiring purpose-built cloud configurations with enhanced input/output

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Rolik, O. I., and S. D. Zhevakin. "COST OPTIMIZATION METHOD FOR INFORMATIONAL INFRASTRUCTURE DEPLOYMENT IN STATIC MULTI-CLOUD ENVIRONMENT." Radio Electronics, Computer Science, Control, no. 3 (November 3, 2024): 160. http://dx.doi.org/10.15588/1607-3274-2024-3-14.

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Context. In recent years, the topic of deploying informational infrastructure in a multi-cloud environment has gained popularity. This is because a multi-cloud environment provides the ability to leverage the unique services of cloud providers without the need to deploy all infrastructure components inside them. Therefore, all available services across different cloud providers could be used to build up information infrastructure. Also, multi-cloud offers versatility in selecting different pricing policies for services across different cloud providers. However, as the number of available cloud

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Thèses sur le sujet "Cloud Infrastructure Optimization"

Zhang, Bo. "Self-optimization of infrastructure and platform resources in cloud computing." Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10207/document.

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L’élasticité est pensée comme une solution importante pour gérer des problèmes de performance dans les systèmes répartis. Toutefois, la plupart des recherches d’élasticité ne concernent que l’approvisionnement de ressources de manière automatique, mais ignorent toujours l’utilisation des ressources provisionnées. Cela pourrait conduire à des fuites de ressources, ce qui entraîne des dépenses inutiles. Pour éviter des problèmes, mes recherches se concentrent donc sur la maximisation de l’utilisation des ressources par l’auto-gestion des ressources. Dans cette thèse, en raison de divers problème

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Salazar, Javier. "Resource allocation optimization algorithms for infrastructure as a service in cloud computing." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB074.

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L’informatique, le stockage des données et les applications à la demande font partie des services offerts par l’architecture informatique en Nuage. Dans ce cadre, les fournisseurs de nuage (FN) agissent non seulement en tant qu’administrateurs des ressources d'infrastructure mais ils profitent aussi financièrement de la location de ces ressources. Dans cette thèse, nous proposons trois modèles d'optimisation du processus d'allocation des ressources dans le nuage dans le but de réduire les coûts générés et d’accroitre la qualité du service rendu. Cela peut être accompli en fournissant au FN les

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Salazar, Javier. "Resource allocation optimization algorithms for infrastructure as a service in cloud computing." Electronic Thesis or Diss., Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB074.

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El, Rachkidi Elie. "Modelling and placement optimization of compound services in a converged infrastructure of cloud computing and internet of things." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLE030/document.

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La convergence de l’Internet des Objets IdO (Internet of Things) et de l’Informatique en Nuage (Cloud Computing) est une approche prometteuse. D’une part, l’Informatique en Nuage fournit des ressources de calcul, de réseau, et de stockage théoriquement illimitées, et d’autre part, l’IdO permet l’interaction des services en nuage avec des objets du monde réel. Une convergence efficace de ces deux technologies aura un impact certainement important sur les innovations dans les domaines des services IT par l’introduction de nouveaux modèles de services d’IdO à la demande. Dans un tel contexte, les

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Sayad, Khaled. "Cross-domain Resilience in Cloud-native, Critical Cyber-Physical Systems Networks : Availability Modeling, Analysis, and Optimization of Critical Services Provisioning." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST028.

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La résilience des Infrastructures Critiques (ICs) est cruciale pour assurer la sécurité et la stabilité socio-économique dans la société moderne. Ces ICs s'appuient sur un réseau complexe de systèmes cyber-physiques (SCPs) couvrant plusieurs domaines tels que les télécommunications et l'énergie, an de garantir un flux continu de services critiques.L'évolution du mode opérationnel des ICs modernes, illustré par l'intégration accrue des technologies cloud-natif dans les réseaux SCPs sous-jacents, introduit de nouveaux dés en termes de résilience face aux cyber-risques, qui s'ajoute au problème d

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Ференс, Дмитро Андрійович. "Дослідження методів розподілу ресурсів критичної ІТ інфраструктури за допомогою технологій штучного інтелекту". Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/23028.

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Проведено аналіз існуючих проблем в управлінні ресурсами ІТ інфраструктури. Запропоновані способи застосування технологій штучного інтелекту для вирішення задачі розподілу ресурсів. Запропоновано модифікований варіант фітнес-функції, що дозволяє здійснювати оптимальний розподіл елементів інфраструктури, враховуючи їх поточне розміщення. Проведено порівняння розроблених алгоритмів з існуючими та підтвердженно високу якість отриманих результатів.<br>The analysis of existing problems in IT infrastructure resources management is carried out. Methods of application of artificial intelligence techno

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Truong, Huu Tram. "Workflow-based applications performance and execution cost optimization on cloud infrastructures." Nice, 2010. http://www.theses.fr/2010NICE4091.

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Les infrastructures virtuelles de cloud sont de plus en plus exploitées pour relever les défis de calcul intensif en sciences comme dans l’industrie. Elles fournissent des ressources de calcul, de communication et de stockage à la demande pour satisfaire les besoins des applications à grande échelle. Pour s’adapter à la diversité de ces infrastructures, de nouveaux outils et modèles sont nécessaires. L’estimation de la quantité de ressources consommées par chaque application est un problème particulièrement difficile, tant pour les utilisateurs qui visent à minimiser leurs coûts que pour les f

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Nasim, Robayet. "Cost- and Performance-Aware Resource Management in Cloud Infrastructures." Doctoral thesis, Karlstads universitet, Institutionen för matematik och datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-48482.

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High availability, cost effectiveness and ease of application deployment have accelerated the adoption rate of cloud computing. This fast proliferation of cloud computing promotes the rapid development of large-scale infrastructures. However, large cloud datacenters (DCs) require infrastructure, design, deployment, scalability and reliability and need better management techniques to achieve sustainable design benefits. Resources inside cloud infrastructures often operate at low utilization, rarely exceeding 20-30%, which increases the operational cost significantly, especially due to energy co

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Merlino, Giovanni. "Sensing and Actuation as a Service, a device-centric paradigm for the IoT: analysis, design and case studies." Doctoral thesis, Università di Catania, 2016. http://hdl.handle.net/10761/3959.

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The huge and steady growth in the number of distributed devices connected to the global network as a so-called Internet of Things (IoT) calls for infrastructure management techniques able to deal with this overwhelming complexity, especially in light of the growing impact of the sharing economy and the role played by the so-called "long tail". In this context, the as-a-Service approach provides well investigated mechanisms for infrastructure and service provisioning, and an interesting challenge lies in evaluating its application to the instantiation and lifecycle management of a dynamic, poss

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Livres sur le sujet "Cloud Infrastructure Optimization"

Smoot, Stephen R., and Nam K. Tan. Private Cloud Computing: Consolidation, Virtualization, and Service-Oriented Infrastructure. Elsevier Science & Technology Books, 2011.

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Hilgurt, S. Ya, and O. A. Chemerys. Reconfigurable signature-based information security tools of computer systems. PH “Akademperiodyka”, 2022. http://dx.doi.org/10.15407/akademperiodyka.458.297.

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The book is devoted to the research and development of methods for combining computational structures for reconfigurable signature-based information protection tools for computer systems and networks in order to increase their efficiency. Network security tools based, among others, on such AI-based approaches as deep neural networking, despite the great progress shown in recent years, still suffer from nonzero recognition error probability. Even a low probability of such an error in a critical infrastructure can be disastrous. Therefore, signature-based recognition methods with their theoretic

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Chapitres de livres sur le sujet "Cloud Infrastructure Optimization"

Lorido-Botran, Tania, Jose Antonio Pascual, Jose Miguel-Alonso, and Jose Antonio Lozano. "Optimization of Application Placement Towards a Greener Cloud Infrastructure." In Applications of Evolutionary Computation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45523-4_56.

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Hou, Xiang, Bin Lin, Rongxi He, Xudong Wang, and Tao Yu. "Infrastructure Deployment and Optimization for Cloud-Radio Access Networks." In Wireless Algorithms, Systems, and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21837-3_20.

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Tarey, Kapil, and Vivek Shrivastava. "Dynamic Priority Based Resource Scheduling in Cloud Infrastructure Using Fuzzy Logic." In Proceedings in Adaptation, Learning and Optimization. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31164-2_42.

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Patel, Minal, Sanjay Chaudhary, and Sanjay Garg. "Performance Modeling and Optimization of Live Migration of Virtual Machines in Cloud Infrastructure." In Research Advances in Cloud Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5026-8_13.

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Goel, Gaurav, Rajeev Tiwari, Raja Kumar Murugesan, Shilpi Harnal, and Shikha Saxena. "Smart grid infrastructure with cloud/fog computing for sustainable development." In Cloud and Fog Optimization-based Solutions for Sustainable Developments. CRC Press, 2024. http://dx.doi.org/10.1201/9781003494430-4.

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Gupta, Punit, S. P. Ghrera, and Mayank Goyal. "QoS Aware Grey Wolf Optimization for Task Allocation in Cloud Infrastructure." In Proceedings of First International Conference on Smart System, Innovations and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5828-8_82.

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Hilarius, Lydia, and Tristan Becker. "Optimization of Cloud Infrastructure Networks with District Heating Integration: A German Case Study." In Lecture Notes in Operations Research. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-58405-3_22.

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Hamza, Muhammad, Muhammad Azeem Akbar, and Rafael Capilla. "Understanding Cost Dynamics of Serverless Computing: An Empirical Study." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53227-6_32.

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AbstractThe advent of serverless computing has revolutionized the landscape of cloud computing, offering a new paradigm that enables developers to focus solely on their applications rather than managing and provisioning the underlying infrastructure. These applications involve integrating individual functions into a cohesive workflow for complex tasks. The pay-per-use model and nontransparent reporting by cloud providers make it difficult to estimate serverless costs, impeding informed business decisions. Existing research studies on serverless computing focus on performance optimization and s

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Qureshi, Salim Raza. "Cache Based Cloud Architecture for Optimization of Resource Allocation and Data Distribution." In ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03107-1_59.

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Nath, Subhrapratim, Arnab Seal, Titir Banerjee, and Subir Kumar Sarkar. "Optimization Using Swarm Intelligence and Dynamic Graph Partitioning in IoE Infrastructure: Fog Computing and Cloud Computing." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6427-2_36.

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Actes de conférences sur le sujet "Cloud Infrastructure Optimization"

Routray, Ramani. "Cloud Storage Infrastructure Optimization Analytics." In 2015 IEEE International Conference on Cloud Engineering (IC2E). IEEE, 2015. http://dx.doi.org/10.1109/ic2e.2015.83.

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Nwanganga, Frederick, Mandana Saebi, Gregory Madey, and Nitesh Chawla. "A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure." In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). IEEE, 2017. http://dx.doi.org/10.1109/cloud.2017.68.

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Gupta, Bhavana, and Nishchol Mishra. "Whale optimization based attack detection on cloud virtualized infrastructure." In LOW RADIOACTIVITY TECHNIQUES 2022 (LRT 2022): Proceedings of the 8th International Workshop on Low Radioactivity Techniques. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0161967.

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Soares, João Antonio, Rafael Burlamaqui Amaral, and Gerson Cavalheiro. "Evaluating SimGrid and CloudSim Plus for Hybrid Cloud Scientific Workflows." In Escola Regional de Redes de Computadores. Sociedade Brasileira de Computação - SBC, 2024. https://doi.org/10.5753/errc.2024.4590.

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Hybrid cloud, integrating public and private clouds, presents a promising environment for scientific applications by combining scalability with cost efficiency. However, the complexity of these environments requires tools to support infrastructure planning and optimization prior to actual deployment. This study evaluates two simulators: SimGrid and CloudSim Plus. The focus is on assessing their suitability for simulating the execution of scientific applications within hybrid cloud environments; particularly regarding scalability. Scientific workflows modeled as directed acyclic graph, were use

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Ilin, Igor, Aleksander Kubarskii, Peter Cornelis Schuur, Aleksander Lepekhin, and Alissa Dubgorn. "Cloud Application For Sheet Materials Cutting Optimization." In DTMIS '20: International Scientific Conference - Digital Transformation on Manufacturing, Infrastructure and Service. ACM, 2020. http://dx.doi.org/10.1145/3446434.3446469.

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Govindarajan, Kannan, and Thamarai Selvi Somasundaram. "A Combinatorial Optimization Algorithm for Load Balancing in Cloud Infrastructure." In 2017 Ninth International Conference on Advanced Computing (ICoAC). IEEE, 2017. http://dx.doi.org/10.1109/icoac.2017.8441410.

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Arora, Manju, Vivek Kumar, and Meenu Dave. "Task Scheduling in Cloud Infrastructure using Optimization Technique Genetic Algorithm." In 2020 Fourth World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4). IEEE, 2020. http://dx.doi.org/10.1109/worlds450073.2020.9210303.

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Caragnano, Giuseppe, Klodiana Goga, Daniele Brevi, Hector Agustin Cozzetti, Olivier Terzo, and Riccardo Scopigno. "A Hybrid Cloud Infrastructure for the Optimization of VANET Simulations." In 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS). IEEE, 2012. http://dx.doi.org/10.1109/cisis.2012.151.

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Ranjit, Mercy Prasanna, Gopinath Ganapathy, Kalaivani Sridhar, and Vikram Arumugham. "Efficient Deep Learning Hyperparameter Tuning Using Cloud Infrastructure: Intelligent Distributed Hyperparameter Tuning with Bayesian Optimization in the Cloud." In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE, 2019. http://dx.doi.org/10.1109/cloud.2019.00097.

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Bernardino, Pedro H., Daniel Sadoc Menasche, and Mario Veiga Pereira. "Matching Computing Requirements of Stochastic Optimization Models and Cloud Computing Resources." In Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2025. https://doi.org/10.5753/wperformance.2025.8294.

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Cloud computing offers scalable solutions for scientific computing, but efficiently allocating resources for stochastic optimization models remains challenging. This work uses real execution data from an energy sector company to develop machine learning models that predict execution time based on algorithm parameters and cloud infrastructure configurations. To optimize resource usage, we propose a utility-based framework that balances execution time and cloud costs. Our results highlight key factors affecting computational efficiency and provide insights for cost-effective resource provisionin

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Rapports d'organisations sur le sujet "Cloud Infrastructure Optimization"

He, Zhitong, Abin Mathew, Abhijeet Ingale, Jue Zhou, Feng Li, and Yaobin Chen. Traffic Management Geocast Study with Connected Vehicles on Indiana Highways. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317753.

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Vehicular communication allows vehicles to interact with road users, roadside infrastructure, and cloud-connected devices. It holds a crucial position in modern transportation systems, impacting both fundamental and advanced aspects and enhancing traffic safety and efficiency. C-V2X is a wireless communication technology that uses cellular networks to enable communication between vehicles and infrastructure. C-V2X can be used for applications such as collision avoidance, traffic management, and remote vehicle diagnostics. This project conducted a feasibility study on the current position of C-

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