Abstract
Edge computing is a promising paradigm that addresses the limitations of traditional cloud-centric architectures by placing computation near the data source. The main challenge is efficient resource allocation for various applications while optimizing resource utilization. This paper proposes a machine learning-based approach for dynamic resource allocation in edge computing environments. It uses machine learning algorithms to analyze real-time data traffic patterns, application demands, and edge node capabilities to dynamically allocate resources like compute, storage, and bandwidth. By continuously acquiring historical data and adjusting to varying workloads, the system improves overall system performance and user experience. Experimental evaluation and simulations show the effectiveness and efficiency of this approach in enhancing resource utilization, lowering latency, and improving scalability in edge computing environments.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Trends and Challenges in Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
