Traffic Engineering for Massive Data Flows
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I3P103Keywords:
Traffic Engineering, Massive Data Flows, Software-Defined Networking (SDN), Network Function Virtualization (NFV), Machine Learning, Flow Optimization, Data Center Networks, Network Scalability, Adaptive Routing, AI-Driven NetworkingAbstract
The exponential growth of data-intensive applications ranging from cloud computing and real-time analytics to IoT and AI-driven workloads has led to unprecedented challenges in managing massive data flows across large-scale network infrastructures. Traditional traffic engineering (TE) mechanisms, designed for static and predictable traffic patterns, are increasingly inadequate in ensuring optimal performance, scalability, and reliability. This research explores modern TE frameworks that integrate software-defined networking (SDN), network function virtualization (NFV), and machine learning (ML) to dynamically optimize traffic flow and resource allocation in high-throughput environments. The paper examines the architectural evolution of TE, advanced optimization algorithms, and adaptive routing techniques capable of responding to real-time network variations. Moreover, it evaluates the trade-offs between centralized and distributed control paradigms in large-scale data centers and backbone networks. By consolidating current advances and identifying persistent challenges, this study provides a comprehensive understanding of next-generation traffic engineering strategies designed to support massive data flows in the era of intelligent and autonomous networksa
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