AI-Driven Optimization in Cloud and Edge Computing: Enhancing Scalability and Performance

Abstract

The rapid expansion of cloud and edge computing technologies has underscored the need for optimizing resource management to enhance scalability and performance. This paper explores the application of AI-driven optimization techniques in cloud and edge environments, focusing on their potential to dynamically allocate resources, manage workloads, and reduce latency. By leveraging machine learning models and predictive analytics, AI can intelligently orchestrate operations, enabling systems to scale efficiently while maintaining high performance. This study examines key optimization strategies such as dynamic workload distribution, energy-efficient resource allocation, and real-time decision-making for adaptive scaling. Furthermore, it addresses challenges related to AI integration, including data consistency, security, and real-time responsiveness in distributed systems. The findings suggest that AI-driven optimization not only enhances operational efficiency but also improves user experience by ensuring low latency, high availability, and efficient resource utilization in cloud and edge computing environments.