Abstract—As cloud computing and data centers become integral to global Information Technology (IT) infrastructure, optimizing routing and resource management in these networks is critical for maintaining performance, scalability, and energy efficiency. This paper presents a structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025. Key advancements include adopting Software-Defined Networking (SDN), machine learning-based routing algorithms, and energy-efficient resource allocation strategies. This paper critically analyzes the challenges posed by scalability, latency, energy consumption, security, and interoperability, alongside the opportunities presented by Artificial Intelligence (AI)-driven autonomous networks, edge computing, and the integration of 5G technologies. Comparative evaluation highlights key trade-offs between performance gains and real-world feasibility, particularly for machine learning and deep reinforcement learning approaches. Furthermore, the study examines emerging trends, including cloud-edge collaboration and multi-objective optimization frameworks. The findings reveal that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations. Overall, this review provides a consolidated perspective on current approaches, open challenges, and promising research directions for next-generation cloud and data center network optimization.
 
Keywords—cloud and data networks, routing, resource management, optimizing next-generation, emerging technologies


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