Load Balancing in DCN Servers Through Software Defined Network Machine Learning
Beissenova G. Zhidebayeva A. Kopzhassarova Z. Kozhabekova P. Myrzakhmetova B. Kerimbekov M. Ussipbekova D. Yeshenkozhaev N.
2024Science and Information Organization
International Journal of Advanced Computer Science and Applications
2024#15Issue 2509 - 519 pp.
In this research paper, we delve into the innovative realm of optimizing load balancing in Data Center Networks (DCNs) by leveraging the capabilities of Software-Defined Networking (SDN) and machine learning algorithms. Traditional DCN architectures face significant challenges in handling unpredictable traffic patterns, leading to bottlenecks, network congestion, and suboptimal utilization of resources. Our study proposes a novel framework that integrates the flexibility and programmability of SDN with the predictive and analytical prowess of machine learning. We employed a multi-layered methodology, initially constructing a virtualized environment to simulate real-world DCN traffic scenarios, followed by the implementation of SDN controllers to instill adaptiveness and programmability. Subsequently, we integrated machine learning models, training them on a substantial dataset encompassing diverse traffic patterns and network conditions. The crux of our approach was the application of these trained models to anticipate network congestion and dynamically adjust traffic flows, ensuring efficient load distribution among servers. A comparative analysis was conducted against prevailing load balancing methods, revealing our models superiority in terms of latency reduction, enhanced throughput, and improved resource allocation. Furthermore, our research illuminates the potential for machine learnings self-learning mechanism to foresee and adapt to future network states or exigencies, marking a significant advancement from reactive to proactive network management. This convergence of SDN and machine learning, as demonstrated, ushers in a new era of intelligent, scalable, and highly reliable DCNs, demanding further exploration and investment for future-ready data centers.
DCN , deep learning , load balancing , machine learning , server , software , Software defined network
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M. Auezov South Kazakhstan University, Shymkent, Kazakhstan
University of Friendship of People’s Academician A. Kuatbekov, Shymkent, Kazakhstan
South Kazakhstan Pedagogical University, Shymkent, Kazakhstan
Kazakh National Medical University named after S. D. Asfendiyarov, Kazakhstan
M. Auezov South Kazakhstan University
University of Friendship of People’s Academician A. Kuatbekov
South Kazakhstan Pedagogical University
Kazakh National Medical University named after S. D. Asfendiyarov
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