Adaptive Load Balancing in Cloud Networks for Enhanced Performance and Reliability

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Awais Rafique

Abstract

Adaptive load balancing in cloud networks plays a critical role in ensuring optimal resource utilization, minimizing latency, and enhancing overall system performance. As cloud computing continues to grow in complexity and scale, the need for intelligent and dynamic load balancing mechanisms becomes increasingly vital. This paper explores adaptive load balancing strategies that leverage real-time data and machine learning algorithms to efficiently distribute workloads across cloud resources. By analyzing and adapting to changing traffic patterns, these approaches reduce bottlenecks and ensure higher reliability and performance. The paper also discusses key methodologies, including dynamic resource allocation, predictive analytics, and automated decision-making, which collectively improve the efficiency and resilience of cloud infrastructures. Through comprehensive analysis and case studies, we demonstrate how adaptive load balancing enhances fault tolerance, reduces downtime, and improves user experience.

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How to Cite
Rafique, A. (2024). Adaptive Load Balancing in Cloud Networks for Enhanced Performance and Reliability. Pioneer Research Journal of Computing Science, 1(2), 22–27. Retrieved from http://prjcs.com/index.php/prjcs/article/view/48

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