Artificial Intelligence and Machine Learning for Network Optimization and Management

EasyChair Preprint 14100

Abstract

This research delves into the application of artificial intelligence (AI) and machine learning (ML) for the optimization and management of modern communication networks. With the exponential growth in data traffic and the increasing complexity of network architectures, traditional methods of network management and optimization are proving inadequate. AI and ML offer novel approaches to address these challenges by enabling intelligent, adaptive, and automated network solutions. The study explores various AI and ML techniques, including supervised and unsupervised learning, reinforcement learning, and deep learning, and their applications in traffic prediction, resource allocation, fault detection, and self-healing networks. It also addresses the integration of AI/ML algorithms with network management systems, examining issues related to scalability, real-time processing, and security. Through simulation and real-world case studies, the research demonstrates the potential of AI and ML to enhance network performance, reduce operational costs, and improve overall service quality. This work highlights the transformative impact of AI and ML on network optimization and management, emphasizing their critical role in the evolution of next-generation communication networks.

Keyphrases: Artificial Intelligence, Network Management, Scalability, fault detection, machine learning, network optimization, real-time processing, resource allocation, self-healing networks, traffic prediction

@booklet{EasyChair:14100,
  author    = {Dylan Stilinki and Saleh Mohamed},
  title     = {Artificial Intelligence and Machine Learning for Network Optimization and Management},
  howpublished = {EasyChair Preprint 14100},
  year      = {EasyChair, 2024}}