Results 231 to 240 of about 3,001,389 (308)

Leveraging Deep Reinforcement Learning for Traffic Engineering: A Survey

IEEE Communications Surveys and Tutorials, 2021
After 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
semanticscholar   +1 more source

A Multi-agent Reinforcement Learning Perspective on Distributed Traffic Engineering

IEEE International Conference on Network Protocols, 2020
Traffic engineering (TE) in multi-region networks is a challenging problem due to the requirement that each region must independently compute its routing decisions based on local observations, yet with the goal of optimizing global TE objectives ...
Nan Geng   +4 more
semanticscholar   +1 more source

Traffic Engineering in Partially Deployed Segment Routing Over IPv6 Network With Deep Reinforcement Learning

IEEE/ACM Transactions on Networking, 2020
Segment Routing (SR) is a source routing paradigm which is widely used in Traffic Engineering (TE). By using SR, a node steers a packet through an ordered list of instructions called segments.
Ying Tian   +6 more
semanticscholar   +1 more source

The Joint Optimization of Online Traffic Matrix Measurement and Traffic Engineering For Software-Defined Networks

IEEE/ACM Transactions on Networking, 2020
Software-Defined Networking (SDN) provides programmable, flexible and fine-grained traffic control capability, which paves the way for realizing dynamic and high-performance traffic measurement and traffic engineering.
Xiong Wang   +6 more
semanticscholar   +1 more source

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