AI-driven resource management strategies for cloud computing systems, services, and applications
Principal Data Engineer (Bigdata and Cloud), USA.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 11(02), 559–566.
Article DOI: 10.30574/wjaets.2024.11.2.0137
Publication history:
Received on 04 March 2024; revised on 15 April 2024; accepted on 17 April 2024
Abstract:
Cloud computing is a way for businesses and individuals to It has changed and revolutionized the way we access and use resources. However, efficient resource management in cloud computing systems remains a major challenge due to the scalability, heterogeneity, and dynamic nature of these environments. To address these challenges, artificial intelligence (AI) technology has emerged as an effective solution to improve resource management efficiency. This paper provides an overview of AI-based strategies for efficient resource management in cloud computing systems, services, and applications.
This paper first reviews resource management challenges in cloud computing, including scalability, heterogeneity, quality of service requirements, and cost optimization. Below is an overview of the various AI techniques used for resource management. B. Algorithms for machine learning, reinforcement learning, predictive analytics, natural language processing, and genetic algorithms.
Next, this paper considers specific AI-based strategies for efficient resource management. These strategies include automated resource provisioning and scaling, intelligent workload planning and task allocation, predictive maintenance and fault detection, and energy-efficient resource management. We also present case studies and applications of AI-driven resource management in various cloud computing scenarios, including large-scale cloud providers, edge computing, serverless computing, and container environments.
This paper describes evaluation metrics and performance analysis techniques to evaluate the effectiveness of AI-based resource management approaches. It highlights the importance of ethical considerations, transparency, and explainability in AI-powered resource management systems. Additionally, the integration of AI technologies into existing resource management frameworks is discussed, and future directions are identified, including B. real-time resource optimization and coordination.
Keywords:
Artificial Intelligence; Resources; Cloud Computing; Efficiency; Cost of Optimization; Quality of Service (QoS)
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0