Optimization and Management of Data Center Networks: A Scoping Review on Key Themes, Challenges, and Artificial Intelligence and Machine Learning Approaches
Kogul Srikandabala, Kirishnni Prabagar, Shalinka Jayatilleke, Pearlie Zhang, Richard Ellerbrock, Sean Rinas, Daswin De Silva, Damminda Alahakoon · IEEE Access · 2025
Data Center Networks (DCNs) have a critical role in enabling scalable, efficient, and reliable digital infrastructures. This scoping review presents recent advancements made in optimization and management strategies for DCNs, with a particular focus on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques. This study follows a structured methodology aligned with the PRISMA-ScR protocol and extends it using Large Language Models (LLMs) to support thematic classification and relevance screening. The analysis identifies seven principal research domains: optical networking, congestion control, flow scheduling, load balancing, Software-Defined Networking (SDN), fault tolerance, and energy-efficient resource management.Within these domains, the study contrasts traditional approaches with emerging AI-driven methods, highlighting both their potential and their limitations. Notably, the findings reveal a lack of integrated, end-to-end AI/ML frameworks capable of addressing the multifaceted demands of modern DCN environments. Additionally, the review highlights the need for scalable, explainable, and context-aware solutions that align with evolving DCN requirements. By analyzing the current body of knowledge and highlighting the critical research gaps, this work contributes a comprehensive foundation for future investigations into intelligent DCN design and operation.