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Studies on network traffic engineering exploiting dynamic IP-flow characteristics
佳憲 北辻
openalex
A feedback queueing network model for traffic signal control at intersections considering congestion propagation in dynamic stochastic environments. [PDF]
Zhao B, Ju Y, Jiao S, Yang D.
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Translation specification of OSPFv2 Traffic Engineering LSAs to NDL
Jeroen van der Ham
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Leveraging Deep Reinforcement Learning for Traffic Engineering: A Survey
IEEE Communications Surveys and Tutorials, 2021After decades of unprecedented development, modern networks have evolved far beyond expectations in terms of scale and complexity. In many cases, traditional traffic engineering (TE) approaches fail to address the quality of service (QoS) requirements of
Yang Xiao, Jun Liu, Jiawei Wu, N. Ansari
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