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|Title:||NetworkAI: An Intelligent Network Architecture for Self-Learning Control Strategies in Software Defined Networks|
|Keywords:||NetworkAI;Software Deﬁned Networks;In-band Network Telemetry;Deep Reinforcement Learning|
|Citation:||IEEE Internet of Things Journal, 2018|
|Abstract:||The past few years have witnessed a wide deployment of software deﬁned networks facilitating a separation of the control plane from the forwarding plane. However, the work on the control plane largely relies on a manual process in conﬁguring forwarding strategies. To address this issue, this paper presents NetworkAI, an intelligent architecture for self-learning control strategies in SDN networks. NetworkAI employs deep reinforcement learning and incorporates network monitoring technologies such as the in-band network telemetry to dynamically generate control policies and produces a near optimal decision. Simulation results demonstrated the effectiveness of NetworkAI.|
|Appears in Collections:||Dept of Electronic and Computer Engineering Research Papers|
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