2025· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
This study proposes a Context-Aware AI framework for dynamic cloud resource management that incorporates workload patterns, user behavior, network conditions, infrastructure health, and business objectives and provides a foundation for future technologies such as edge computing, IoT, 6G networks, and intelligent enterprise applications.
Abstract
Cloud computing provides scalable and cost-effective resources for modern digital enterprises, but increasing workload diversity, changing user demands, and complex infrastructures make resource management challenging. Traditional resource allocation methods often fail to adapt to dynamic cloud environments, resulting in inefficient resource usage, SLA violations, and higher operational costs. This study proposes a Context-Aware AI framework for dynamic cloud resource management that incorporates workload patterns, user behavior, network conditions, infrastructure health, and business objectives. The framework combines context acquisition, real-time analytics, Long Short-Term Memory (LSTM) workload prediction, Deep Reinforcement Learning (DRL)-based optimization, and adaptive orchestration. Experimental results show improved resource utilization, response time, energy efficiency, cost reduction, and service reliability. The framework supports autonomous cloud management and provides a foundation for future technologies such as edge computing, IoT, 6G networks, and intelligent enterprise applications.
This research proposes an AI-driven resource scheduling framework that integrates workload prediction, resource classification, intelligent scheduling, and continuous feedback mechanisms that aims to optimize multiple objectives, including cost reduction, execution efficiency, energy consumption, and SLA compliance.
Michael Anderson· International Journal of App...· 0 citations
In conclusion, the proposed AI-based workload prediction framework significantly enhances cloud resource management by accurately forecasting future workload demands and enabling proactive resource allocation. By utilizing machine learning and deep learning techniques such as LSTM, Random Forest Regression, and Gradient Boosting, the system improves resource utilization, reduces response time, lowers operational costs, and minimizes SLA violations. The results demonstrate superior prediction accuracy compared to traditional methods, leading to better Quality of Service (QoS) and energy efficiency. This study highlights the potential of AI-driven predictive analytics to transform cloud computing from reactive resource management to intelligent, autonomous, and adaptive cloud ecosystems. Future research can further improve performance through the integration of federated learning, reinforcement learning, and edge-cloud computing technologies.
John Peterson· International Journal of App...· 0 citations
This study presents an efficient algorithm for large-scale resource management in a multi-tenant cloud environment that integrates intelligent resource scheduling, workload balancing and adaptive virtual machine allocation to optimize resource utilization while satisfying multiple performance objectives.
Onwuegbuchulem Gift., Bennett E.O., M. D. et al.· International journal of re...· 0 citations
Reinforcement learning-based adaptive resource management framework is proposed that enables cloud systems to autonomously learn optimal resource allocation policies through continuous interaction with the environment and significantly outperforms static and reactive baseline strategies in terms of resource utilization efficiency and response time stability.
Rajesh Sharma, Priya Natarajan· International Journal of Mac...· 0 citations
This work proposes a scalable, intelligent, and resilient foundation for next-generation high-performance analytics and data-intensive applications that integrates adaptive resource management, intelligent workload scheduling, dynamic task migration, predictive analytics, and machine learning-based optimization to improve computational efficiency and responsiveness.
John Peterson, L. Martínez· International Journal of App...· 0 citations