Utilizing Machine Learning Algorithms for Optimization and Management of Cloud Network Performance

Abstract

Optimizing and managing cloud network performance is crucial for ensuring efficient resource utilization and meeting user expectations. Traditional methods often struggle to adapt to the dynamic and complex nature of cloud environments. This paper explores the application of machine learning (ML) algorithms for enhancing cloud network performance through proactive management and optimization strategies. By leveraging ML techniques such as predictive analytics, anomaly detection, and adaptive resource allocation, cloud providers can dynamically adjust network configurations and resource allocations based on real-time data and trends. Case studies and experiments demonstrate the efficacy of ML-driven approaches in improving network throughput, latency management, and overall reliability. Furthermore, the integration of ML algorithms enables automated decision-making processes that optimize QoS parameters while minimizing operational costs. This research contributes to advancing the state-of-the-art in cloud network management by highlighting the transformative potential of ML in addressing performance challenges and enhancing scalability in cloud computing infrastructures.