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Готові списки джерел за темами / Cloud Infrastructure Optimization / Статті в журналах

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Статті в журналах з теми "Cloud Infrastructure Optimization"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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Анотація:

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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11

Vats, Rahul. "CLOUD INFRASTRUCTURE OPTIMIZATION FOR LARGE-SCALE BANKING MERGERS." INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND MANAGEMENT INFORMATION SYSTEMS 16, no. 2 (2025): 1219–31. https://doi.org/10.34218/ijitmis_16_02_076.

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12

Karthik Reddy Mannem. "Trends in cloud infrastructure optimization: Balancing cost, performance and security." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 1097–107. https://doi.org/10.30574/wjarr.2025.26.2.1634.

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This article explores the evolution of cloud infrastructure optimization strategies as organizations navigate increasingly complex multi-cloud and hybrid environments. As cloud adoption accelerates across industries, the focus has shifted from basic cost management to sophisticated optimization frameworks that balance financial efficiency with performance requirements and security considerations. The article examines several transformative trends, including the progression from reactive to predictive auto-scaling mechanisms that anticipate resource needs before demand spikes occur. It investig

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13

Cortés-Mendoza, Jorge M., Andrei Tchernykh, Fermin A. Armenta-Cano, Pascal Bouvry, Alexander Yu Drozdov, and Loic Didelot. "Biobjective VoIP Service Management in Cloud Infrastructure." Scientific Programming 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/5706790.

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Voice over Internet Protocol (VoIP) allows communication of voice and/or data over the internet in less expensive and reliable manner than traditional ISDN systems. This solution typically allows flexible interconnection between organization and companies on any domains. Cloud VoIP solutions can offer even cheaper and scalable service when virtualized telephone infrastructure is used in the most efficient way. Scheduling and load balancing algorithms are fundamental parts of this approach. Unfortunately, VoIP scheduling techniques do not take into account uncertainty in dynamic and unpredictab

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14

Wu, Shuyou, Zhengxiao Wu, Xiaohong Wu, Jie Tao, and Yonggen Gu. "Queuing-Based Federation and Optimization for Cloud Resource Sharing." Information 13, no. 8 (2022): 361. http://dx.doi.org/10.3390/info13080361.

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Resource sharing can gain economies of scale and increase utilization of cloud infrastructure, a critical challenge of which is how to design efficient resource sharing solutions among self-interested cloud providers. Cloud federation can realize resource sharing, but the existing methods of forming federation need complex computation to guarantee the stability of federation. To address this shortcoming, after analyzing an optimal allocation approach of service requests among clouds, we propose a pareto optimal resource sharing solution named Cloud Light-Federation Sharing (CLFS), in which eac

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15

Kalaskar, Chetankumar, and S. Thangam. "Fault Tolerance of Cloud Infrastructure with Machine Learning." Cybernetics and Information Technologies 23, no. 4 (2023): 26–50. http://dx.doi.org/10.2478/cait-2023-0034.

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Abstract Enhancing the fault tolerance of cloud systems and accurately forecasting cloud performance are pivotal concerns in cloud computing research. This research addresses critical concerns in cloud computing by enhancing fault tolerance and forecasting cloud performance using machine learning models. Leveraging the Google trace dataset with 10000 cloud environment records encompassing diverse metrics, we systematically have employed machine learning algorithms, including linear regression, decision trees, and gradient boosting, to construct predictive models. These models have outperformed

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16

Goyal, Shanky, Shashi Bhushan, Yogesh Kumar, et al. "An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm." Sensors 21, no. 5 (2021): 1583. http://dx.doi.org/10.3390/s21051583.

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Анотація:

Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubtedly an innovation as the investment in the real and physical infrastructure is much greater than the cloud technology investment. The present work addresses the issue of power consumption done by cloud infrastructure. As there is a need for algorithms and techniques that can reduce energy consumption and schedule resource for the effectiveness of servers. Loa

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17

Uday Kiran Yedluri. "Navigating a career in cloud and infrastructure engineering: Insights and best practices." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 842–51. https://doi.org/10.30574/wjarr.2025.26.1.1106.

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Анотація:

This comprehensive article examines the evolving landscape of cloud and infrastructure engineering, focusing on transforming traditional IT infrastructure through cloud adoption. The article explores key trends in cloud service models, technical competencies required for cloud professionals, and the impact of certifications on career advancement. The article covers emerging technologies such as infrastructure automation, edge computing, and FinOps practices while also investigating the changing role of infrastructure engineers in strategic planning and performance optimization. Examining indus

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18

Researcher. "CLOUD ADAPTATION: NAVIGATING THE MIGRATION LANDSCAPE." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 874–81. https://doi.org/10.5281/zenodo.13929366.

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This article explores the accelerating trend of organizations transitioning from traditional datacenter architectures to cloud-based solutions, driven by the expanding global cloud computing market and the need for flexible, scalable IT infrastructure. It examines key considerations for cloud migration, including workload characteristics, regulatory compliance, cost optimization, performance requirements, security considerations, and existing IT infrastructure. The article delves into critical aspects such as cloud security, data compliance, and cost and performance optimization, providing ins

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19

Saili Krishna Maliye. "Multi-Cloud Automation : A Strategic Approach to Cloud Infrastructure Management." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 183–90. http://dx.doi.org/10.32628/cseit24106167.

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This article examines the evolution and impact of multi-cloud automation strategies in modern enterprise environments, supported by comprehensive industry research and case studies. Drawing from multiple industry reports, including Flexera's 2024 State of the Cloud Report and PwC's Cloud and AI Business Survey, the research reveals that 89% of enterprises now employ multi-cloud strategies, with the global multi-cloud management market projected to grow at a CAGR of 27.3% through 2030. The article analyzes key components of successful multi-cloud automation, including deployment automation, ope

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20

Sakhamuri, Naga Sai Bandhavi. "Quantum-Inspired Optimization of Cloud Infrastructure for Reliability and Cost Efficiency." European Journal of Computer Science and Information Technology 13, no. 40 (2025): 163–86. https://doi.org/10.37745/ejcsit.2013/vol13n40163186.

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Quantum-inspired optimization emerges as a transformative paradigm for cloud infrastructure management, addressing the increasing complexity and multi-dimensional challenges faced by modern distributed systems. This article introduces a comprehensive framework that applies quantum computational principles to classical infrastructure, enabling more efficient navigation of complex solution landscapes without requiring quantum hardware. The framework targets critical operational areas including workload distribution, auto-scaling, resource allocation, and fault tolerance enhancement. By leveragin

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21

Researcher. "DEMYSTIFYING CLOUD SERVICE PROVIDERS AND INFRASTRUCTURE AS CODE." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 452–63. https://doi.org/10.5281/zenodo.13285378.

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Анотація:

This article explores the transformative impact of cloud computing and Infrastructure as Code (IaC) on modern IT practices. It delves into the fundamentals of cloud services, examining the offerings of major providers like AWS, Azure, and Google Cloud Platform, and their role in democratizing access to advanced computing capabilities. The article further investigates how IaC is revolutionizing infrastructure management by treating configuration as software, enabling automation, consistency, and version control. Through a real-world scenario, the synergy between cloud services and IaC is demons

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22

Alhazeem, Housam Ghanim. "Principles of Cloud Computing Infrastructure IaaS." Journal of engineering sciences and information technology 8, no. 2 (2024): 21–26. http://dx.doi.org/10.26389/ajsrp.a010123.

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Анотація:

Infrastructure as a Service (IaaS) represents a cornerstone in contemporary cloud computing, providing essential on-demand computing resources, including servers, storage, and networking. This paper explores the foundational principles of IaaS, highlighting its ability to facilitate dynamic server management through practices like auto-scaling and ephemeral server utilization. The use of custom images in IaaS ensures minimal downtime and scalable deployment, while loose coupling enhances fault tolerance and adaptability. Emphasizing high availability through auto-scaling, IaaS supports continu

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23

Miroshnychenko, Vitalii. "Conceptual Models for Optimizing Infrastructure Solutions for Isps Based on Cloud Technologies." American Journal of Engineering and Technology 07, no. 06 (2025): 08–13. https://doi.org/10.37547/tajet/volume07issue06-02.

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Анотація:

This study examines the conceptual models for optimizing infrastructure solutions for ISPs based on cloud technologies. The relevance of this research is justified by the rapid technological advancements that serve as the foundation for infrastructure solutions in internet service providers (ISPs). Their optimization requires a systematic approach that considers load balancing, distributed data storage, security issues, and regulatory aspects. However, there are contradictions in the scientific literature regarding optimization methods. The goal of this article is to systematize the understand

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24

Shravan Kumar Amjala. "AI-Powered Cloud Automation: A Scholarly Perspective." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2664–72. https://doi.org/10.30574/wjaets.2025.15.2.0845.

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Анотація:

This article explores the transformative integration of artificial intelligence and machine learning into cloud infrastructure, creating a paradigm shift in enterprise IT operations. As organizations increasingly migrate to distributed cloud environments, the inherent complexity demands sophisticated automation beyond traditional manual capabilities. AI-powered cloud automation addresses these challenges through intelligent orchestration, predictive resource scaling, and autonomous optimization mechanisms. The synergistic relationship between AI and cloud technologies enables self-optimizing s

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25

Subhasis, Kundu. "AI-Generated Predictive Cloud Optimization: Preemptively Detecting and Preventing System Failures for Enhanced Cloud Reliability." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 11, no. 6 (2023): 1–6. https://doi.org/10.5281/zenodo.15084283.

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This study examines the application of AI-driven predictive cloud optimization to enhance cloud reliability by forecasting and preventing system failures. An innovative method is proposed, employing machine learning algorithms to analyze extensive cloud infrastructure data, identify potential issues, and implement proactive measures. This approach integrates real-time monitoring, predictive analytics, and automated solutions to minimize downtime and improve resource management. A case study is presented, demonstrating the method's success in a large-scale cloud environment, with significant im

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26

Navya Krishna Alapati. "Real-time data analytics and processing for adaptive load balancing in cloud infrastructures." World Journal of Advanced Engineering Technology and Sciences 14, no. 3 (2025): 538–46. https://doi.org/10.30574/wjaets.2025.14.3.0179.

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Анотація:

Dynamic load balancing is a key challenge in AI-enabled cloud infrastructures with volatile resource demand. This results in resource utilization drifting away from balance and creating performance loss, so the infrastructure starts to operate inefficiently. In this paper, we introduce a principled approach based on reinforcement learning and algorithmic optimization to dynamically allocate the load across the infrastructure. Our approach is based on reinforcement learning, providing instructions on what the ideal actions for load balancing in an ever-changing environment are. It takes advanta

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27

Meikshan, Vladimir, and Natalia Teslya. "Selection of optimal data placement using cloud infrastructure." Proceedings of the Russian higher school Academy of sciences, no. 2 (July 21, 2021): 34–42. http://dx.doi.org/10.17212/1727-2769-2021-2-34-42.

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Анотація:

Benefits of using cloud technology are obvious, their application is expanding, as a result, it determines the steady growth of demand. Cloud computing has acquired particular relevance for large companies connected with Internet services, retailing, logistics that generate large volume of business and other information. The use of cloud technologies allows organizing the joint consumption of resources, solving the problems of storing and transferring significant amounts of data. Russian consumer cooperation refers to large territory distributed organizations actively forming their own digital

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28

Isaac, Clement Praveen Xavier Pakkam. "No-Code Cloud AI: The Rise of AI-Assisted Cloud Architecture Design." International Journal of Engineering and Advanced Technology Studies 13, no. 2 (2025): 1–21. https://doi.org/10.37745/ijeats.13/vol13n2121.

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The rapid evolution of cloud computing has transformed it from a specialized technical domain into a strategic business necessity. However, the complexity of cloud infrastructure design traditionally demands deep expertise in networking, security, and resource provisioning—creating a significant barrier for many organizations pursuing digital transformation. This article explores how an emerging paradigm—No-Code Cloud AI—is bridging this expertise gap by democratizing access to sophisticated cloud infrastructure through AI-assisted design tools. It introduces the Three-Tier No-Code AI Cloud Fr

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29

Saad-Eddine, Chafi, Balboul Younes, Fattah Mohammed, Mazer Said, El Bekkali Moulhime, and Bernoussi Benaissa. "Resource placement strategy optimization for IoT oriented monitoring application." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 4 (2022): 788–96. https://doi.org/10.12928/telkomnika.v20i4.23762.

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Cloud computing and the low power wide area network (LPWAN) network represent the key infrastructures for developing intelligent solutions based on the internet of things (IoT). However, the diversity of use cases and deployment scenarios of IoT in the different domains makes optimizing IoT-based cloud solutions a major challenge. The cloud solution’s cost increases with the increase in central processing unit (CPU) resources and energy consumption. The optimal use of edge material resources in industrial solutions will reduce the consumption of resources and thus optimize cloud infrastr

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30

Koneru, Sri Harsha. "Cloud-Based Digital Twins: Revolutionizing Endpoint Infrastructure Management." European Journal of Computer Science and Information Technology 13, no. 23 (2025): 96–114. https://doi.org/10.37745/ejcsit.2013/vol13n2396114.

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This article explores the emerging paradigm of cloud-based digital twins for endpoint infrastructure simulation, which represents a significant advancement in enterprise IT management. In today's complex enterprise environments characterized by distributed workforces and diverse device ecosystems, organizations face mounting challenges in managing endpoint infrastructure securely and efficiently. Digital twins—virtual replicas of physical endpoint environments—enable IT teams to conduct comprehensive testing of updates, security controls, and configuration changes before deployment to producti

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31

Amandeep Singh Saini. "The Rise of Cloud Computing and the Importance of IaC." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1756–64. https://doi.org/10.32628/cseit251112156.

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Анотація:

Infrastructure as Code (IaC) has emerged as a transformative approach in cloud computing, revolutionizing how organizations manage and deploy their infrastructure. By treating infrastructure configurations as software code, IaC enables automated, consistent, and scalable deployment processes across multiple cloud environments. The shift from traditional manual infrastructure management to automated IaC practices has significantly enhanced operational efficiency, reduced human errors, and strengthened security postures. Organizations leveraging IaC benefit from rapid deployment capabilities, st

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32

Rajesh Daruvuri. "Dynamic load balancing in AI-enabled cloud infrastructures using reinforcement learning and algorithmic optimization." World Journal of Advanced Research and Reviews 20, no. 1 (2023): 1327–35. https://doi.org/10.30574/wjarr.2023.20.1.2045.

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Анотація:

Dynamic load balancing is a key challenge in AI-enabled cloud infrastructures with volatile resource demand. This results in resource utilization drifting away from balance and creating performance loss, so the infrastructure starts to operate inefficiently. In this paper, we introduce a principled approach based on reinforcement learning and algorithmic optimization to dynamically allocate the load across the infrastructure. Our approach is based on reinforcement learning, providing instructions on what the ideal actions for load balancing in an ever-changing environment are. It takes advanta

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33

Prasen Reddy Yakkanti. "AI-Driven Infrastructure Scaling for Cost Optimization in Cloud Environments: A Systematic Review." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2685–93. https://doi.org/10.32628/cseit25112741.

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Анотація:

This article comprehensively analyzes AI-driven infrastructure scaling for cost optimization in cloud environments. We examine how machine learning algorithms can dynamically adjust cloud resources based on historical patterns and real-time workload demands, addressing the persistent challenge of balancing performance requirements with cost efficiency. The article analyzes various scaling mechanisms, including historical pattern analysis, real-time monitoring systems, and decision-making algorithms for resource adjustment, alongside predictive analytics approaches for workload forecasting. Thr

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34

Haragi L, Darshan, Mahith S, and Prof Sahana B. "Infrastructure Optimization in Kubernetes Cluster." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 546–55. http://dx.doi.org/10.51201/jusst/21/05292.

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Анотація:

Kubernetes is a compact, extensible, open-source stage for overseeing containerized responsibilities and administrations, that works with both decisive setup and robotization. Kubernetes is like VMs, however having loosened up isolation properties to share the Operating System (OS) among the applications. The container conversely with VM, has its own document framework, a portion of Central Processing Unit(CPU), memory, process space, and much more. Kubernetes cluster is a bunch of node machines for running containerized applications. Each cluster contains a control plane and at least one node

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35

Oghale Joel, B. Micah, Ubamadu Bright Chibunna, and Andrew Ifesinachi Daraojimba. "Cyber Cloud Framework: Integrating Cyber Security Resilience into Cloud Infrastructure Optimization for Enhanced Operational Efficiency." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 1 (2024): 1378–82. https://doi.org/10.54660/.ijmrge.2024.5.1.1378-1382.

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Анотація:

The increasing adoption of cloud computing has revolutionized the way organizations manage their data and operations, offering unparalleled scalability and flexibility. However, this digital transformation brings along significant cyber security challenges, with cloud environments becoming prime targets for cyber threats. In response, the CyberCloud Framework emerges as a pioneering approach that seamlessly integrates cyber security resilience into cloud infrastructure optimization to enhance operational efficiency. This abstract presents an overview of the CyberCloud Framework, outlining its

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36

Irina, Potapova. "State Strategy of Russian Universities and Technological Business Companies for the Transfer of Bioinformatics Knowledge." International Journal of Applied Research in Bioinformatics 9, no. 2 (2019): 50–56. http://dx.doi.org/10.4018/ijarb.2019070105.

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This article presents examples of optimization of IT-infrastructure. Issues of information security at the time of digital economy are discussed. The article lists modern trends of optimization at IT-infrastructure use of bioinformatics knowledge. Examples of cloud services and the use of hybrid clouds are given. The information system of the future is defined. There is an integration of the existing system into a single digital platform and a description is given of ensuring the work of the social bloc of the country. The author defines the software visualizer, which opens unlimited possibili

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Mithun Kumar Pusukuri. "Leveraging Observability-Driven Predictive Analytics for Cost-Effective Hybrid Cloud Migration on AWS." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1760–67. https://doi.org/10.32628/cseit241061216.

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This article presents an innovative framework that revolutionizes hybrid cloud migration strategies by integrating advanced observability tools with predictive analytics capabilities on AWS infrastructure. The article introduces a comprehensive approach that combines real-time monitoring, machine learning-driven prediction models, and automated risk mitigation strategies to optimize migration success rates and reduce operational costs. Through extensive experimentation across multiple enterprise environments encompassing numerous virtual machines and applications, the framework demonstrated si

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Kniazhyk, Taras, and Oleksandr Muliarevych. "Cloud Computing With Resource Allocation Based on Ant Colony Optimization." Advances in Cyber-Physical Systems 8, no. 2 (2023): 104–10. http://dx.doi.org/10.23939/acps2023.02.104.

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In this study, we explore the intricacies of cloud computing technologies, with an emphasis on the challenges and concerns pertinent to resource allocation. Three opti- mization techniques—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) — have been meticulously analyzed concerning their applications, objectives, and operational methodologies. The study underscores these algorithms' pivotal role in enhancing cloud resource optimization, while also elucidat- ing their respective merits and limitations. As the complexity of cloud computing escalates, d

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Chaitanya Teja Musuluri. "Strategic Implementation of Cloud Automation for Enhanced Scalability." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 181–88. https://doi.org/10.32996/jcsts.2025.7.2.17.

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The rapid evolution of cloud infrastructure demands effective automation strategies for optimal scalability. Organizations are increasingly adopting serverless computing, microservices architecture, and Infrastructure as Code (IaC) to enhance their cloud operations. The shift towards automated cloud management has revolutionized how businesses handle infrastructure deployment, resource optimization, and system maintenance. Through the implementation of machine learning-driven solutions and advanced automation frameworks, organizations can achieve significant improvements in operational efficie

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Sushant Sood. "AI-Driven Resource Allocation: Revolutionizing Cloud Infrastructure Management." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1652–62. https://doi.org/10.32628/cseit251112194.

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This comprehensive article examines the transformative impact of artificial intelligence on cloud resource management, exploring the evolution from traditional static allocation methods to dynamic, AI-driven approaches. The article investigates core technologies, including machine learning models and real-time decision-making frameworks, while evaluating their applications across virtual machine provisioning, container orchestration, and multi-cloud environments. Through detailed case studies of e-commerce platforms and video streaming services, the article demonstrates significant improvement

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Kuriakose, John Linton. "Optimizing Site Reliability Engineering with Cloud Infrastructure." International Journal of Computational and Experimental Science and Engineering (IJCESEN) 11, no. 02 (2025): 2572–86. https://doi.org/10.22399/ijcesen.1983.

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This is the final published version of the article originally published in the <em>International Journal of Computational and Experimental Science and Engineering (IJCESEN)</em>, Vol. 11, No. 2, 2025. DOI: 10.22399/ijcesen.1983

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Gupta, Punit, Ujjwal Goyal, and Vaishali Verma. "Cost-Aware Ant Colony Optimization for Resource Allocation in Cloud Infrastructure." Recent Advances in Computer Science and Communications 13, no. 3 (2020): 326–35. http://dx.doi.org/10.2174/2213275912666190124101714.

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Background: Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm & Round Robin improve the performance but these are not cost efficient at the same time. Objective: In early proven task scheduling algorithms network cost are not included but in this proposed ACO network overhead or cost is taken into consideration which thus improves the efficiency of the al

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Sourabh Jain. "Performance Optimization of Hybrid Encryption Techniques in Oracle Cloud Infrastructure: A Comparative Study." Panamerican Mathematical Journal 35, no. 3s (2025): 230–50. https://doi.org/10.52783/pmj.v35.i3s.3889.

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Cloud security has become a critical concern due to the increasing volume of sensitive data stored and processed in cloud environments. Traditional encryption methods often fail to balance security, efficiency, and scalability, leading to vulnerabilities in cloud infrastructure. This study explores the performance optimization of hybrid encryption techniques in Oracle Cloud Infrastructure (OCI) by integrating RSA, Blowfish, Homomorphic Encryption, and Blockchain-based key management. The proposed models enhance data confidentiality, secure key exchange, and time-limited access control. Experim

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Manvitha Potluri. "Reinforcement Learning for Self-Optimizing Infrastructure as Code (IaC)." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 651–56. https://doi.org/10.32996/jcsts.2025.7.3.74.

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Reinforcement Learning for Self-Optimizing Infrastructure as Code introduces a paradigm shift that fundamentally transforms cloud operations, moving beyond mere infrastructure improvement to reimagine the entire operational model. This article examines how reinforcement learning techniques create autonomous infrastructure systems that continuously evolve through operational feedback loops, eliminating traditional boundaries between deployment, monitoring, and optimization phases. By replacing manual intervention with intelligent, self-directing systems, RL-based approaches revolutionize how or

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Dmytriv, Yurii, and Mykola Orlov. "Use of artificial intelligence methods and tools in the construction of cloud IT infrastructures." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 17 (June 2025): 101–13. https://doi.org/10.23939/sisn2025.17.101.

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The paper examines the explores the use of artificial intelligence (AI) methods and tools for the efficient construction, management, and optimization of cloud IT infrastructures. The main challenges related to the automation of deployment, scaling, monitoring, and resource optimization in the cloud environment are analyzed, along with the role of AI in addressing these issues. Approaches to integrating AI to improve productivity, reduce operational costs, and enhance the security of cloud platforms are discussed. Special attention is given to the use of machine learning algorithms for load fo

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de Oliveira, Frederico Alvares, and Thomas Ledoux. "Self-management of cloud applications and infrastructure for energy optimization." ACM SIGOPS Operating Systems Review 46, no. 2 (2012): 10–18. http://dx.doi.org/10.1145/2331576.2331579.

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Researcher. "OPTIMIZING HYBRID CLOUD ARCHITECTURES: A COMPREHENSIVE STUDY OF PERFORMANCE ENGINEERING BEST PRACTICES." International Journal of Engineering and Technology Research (IJETR) 9, no. 2 (2024): 288–99. https://doi.org/10.5281/zenodo.13833938.

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Hybrid cloud architectures have emerged as a pivotal solution for organizations seeking to balance the flexibility of public clouds with the control of on-premises infrastructure. However, these complex environments present unique challenges in maintaining optimal performance across diverse systems. This article examines the critical aspects of performance engineering in hybrid cloud environments, offering a comprehensive analysis of best practices and real-world implementations. We explore key strategies for optimizing workload distribution, minimizing latency, managing resources effecti

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Natta, Prasanna Kumar. "AI-Powered Cloud Orchestration: Automating Multi-Cloud & Hybrid Cloud Workloads." European Journal of Computer Science and Information Technology 13, no. 8 (2025): 138–47. https://doi.org/10.37745/ejcsit.2013/vol13n8138147.

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AI-powered cloud orchestration revolutionizes how enterprises manage and optimize their multi-cloud and hybrid cloud environments. Integrating artificial intelligence into cloud management addresses complexity, manual intervention, and reactive problem-solving challenges that plague traditional orchestration methods. By implementing intelligent algorithms for resource allocation, workload balancing, predictive scaling, security enhancement, and self-healing capabilities, organizations can transform their cloud operations from manually-defined workflows to autonomous systems capable of continuo

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Bhole, Anju. "Cloud Resource Management and Cost Optimization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–7. https://doi.org/10.55041/ijsrem26028.

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Cloud computing has emerged as a pivotal innovation, facilitating businesses in obtaining scalable, adaptable, and economical IT resources. Nonetheless, the intricate nature of cloud infrastructure management may result in unpredictable expenses if resources are not utilized efficiently. As organizations progressively shift their operations to cloud platforms, there is an increasing demand for effective resource management and strategies aimed at cost optimization to avert issues such as over-provisioning, underutilization, and decline in performance. This paper investigates essential strategi

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Praveen Kumar Thota. "Policy-driven decision intelligence models for adaptive AI-native cloud infrastructure." World Journal of Advanced Engineering Technology and Sciences 12, no. 1 (2024): 555–64. https://doi.org/10.30574/wjaets.2024.12.1.0263.

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The digital environment has changed through AI-native cloud infrastructure development which needs sophisticated decision-making frameworks exceeding traditional heuristics with static automation approaches. Artificial intelligence (AI) along with adaptive architectures and policy- based governance systems have produced policy-driven decision intelligence (PDDI) models which operate in the complex and dynamic nature of cloud ecosystems. The models combine machine learning with reinforcement learning and formalized policy constraints to deliver automatic context-aware adaptation to changing wor

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