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Network Intelligent Control and Traffic Optimization Based on SDN and Artificial Intelligence
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Simulated annealing: theory and applications.
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Using artificial intelligence in routing schemes for wireless networks
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Topology Discovery in Software Defined Networks: Threats, Taxonomy, and State-of-the-Art
inferredcites →https://doi.org/10.1109/comst.2017.2707140openalex-snapshot
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems
inferredcites →https://doi.org/10.1109/icc.2014.6883790openalex-snapshot
SDN framework for connectivity services
inferredcites →https://doi.org/10.1109/iscc.2014.6912609openalex-snapshot
Comparison of SDN OpenFlow network simulator and emulators: EstiNet vs. Mininet
inferredcites →https://doi.org/10.1109/jsac.2017.2760418openalex-snapshot
NFV and SDN—Key Technology Enablers for 5G Networks
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Traffic engineering with traditional IP routing protocols
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Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning
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Artificial Intelligence Enabled Routing in Software Defined Networking
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Particle swarm optimization
inferredcites →https://doi.org/10.48550/arxiv.1603.01203openalex-snapshot
Kulfi: Robust Traffic Engineering Using Semi-Oblivious Routing
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Hedera: dynamic flow scheduling for data center networks
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Solving Network Routing Problem Using Artificial Intelligent Techniques
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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