2024· International Journal of Applied Data Science & Modern Computing· Vol 7, pp. 01-16· 0 citations
TL;DR
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 and demonstrates superior prediction accuracy compared to traditional methods.
Abstract
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.
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 enterp...
Richard Evans, Karen Lewis· International Journal of App...· 0 citations
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
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 impr...
John Peterson, L. Martínez· International Journal of App...· 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...
Rajesh K. Sharma, Priya Natarajan· International Journal of Mac...· 0 citations
In modern IT ecosystems, accurately predicting operational costs is critical for budgeting, resource allocation, and service optimization. Traditional cost estimation models often fall short in environments characterized by high variability and scale. This paper presents a machine learning-driven approach to IT cost pr...
Z. Pawlak· International Journal of Dat...· 0 citations
A deep learning-based model for task scheduling in cloud computing that employs a convolutional neural network to predict the optimal machines for task allocation and consumes less energy than other models is proposed, demonstrating its effectiveness in cloud task scheduling.
Kavita Rani, O. Sangwan, R. Garg· IAES International Journal o...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
