Dynamic resource allocation using AI-driven workload forecasting in multi-cloud environments
Independent Researcher, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(01), 3188-3198
Article DOI: 10.30574/wjarr.2024.23.1.2178
Publication history:
Received on 08 June 2024; revised on 22 July 2024; accepted on 28 July 2024
Abstract:
This research investigates the application of artificial intelligence (AI) for dynamic resource allocation using workload forecasting in multi-cloud environments. With the growing adoption of multi-cloud strategies, organizations face increasing challenges in managing resource distribution efficiently due to fluctuating and unpredictable workloads. To address this, the study introduces an AI-driven framework that combines time-series forecasting models such as Long Short-Term Memory (LSTM) networks, reinforcement learning, and decision tree-based algorithms to accurately predict workload demands and allocate resources dynamically across multiple cloud platforms. The system continuously monitors workload patterns and adjusts resource provisioning in real-time to enhance performance and cost-efficiency. Experimental results demonstrate that the proposed approach significantly improves CPU and memory utilization, reduces operational costs by up to 25%, and increases SLA compliance. By offering a scalable, intelligent solution for resource management, this research contributes to the advancement of autonomous cloud operations. It provides practical value for optimizing complex multi-cloud infrastructures' performance, reliability, and efficiency.
Keywords:
Cloud Computing; Multi-Cloud Environments; Resource Allocation; Workload Forecasting; Artificial Intelligence (Ai)
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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