Title
NetworkAI: An Intelligent Network Architecture for Self-Learning Control Strategies in Software Defined Networks
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 software defined networking 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.
Year
DOI
Venue
2018
10.1109/JIOT.2018.2859480
IEEE Internet of Things Journal
Keywords
Field
DocType
Monitoring,Computer architecture,Machine learning,Internet of Things,Process control,Real-time systems
Forwarding plane,Software deployment,Optimal decision,Computer science,Computer network,Process control,Intelligent Network,Network monitoring,Software-defined networking,Distributed computing,Reinforcement learning
Journal
Volume
Issue
ISSN
5
6
2327-4662
Citations 
PageRank 
References 
3
0.37
0
Authors
6
Name
Order
Citations
PageRank
Haipeng Yao114317.59
Tianle Mai2273.43
Xiaobin Xu314522.74
Peiying Zhang47915.21
Maozhen Li51354183.79
Yunjie Liu6726.47