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A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
September 20, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Giorgio Stampa, Marta Arias, David Sanchez-Charles, Victor Muntes-Mulero, Albert Cabellos
arXiv ID
1709.07080
Category
cs.NI: Networking & Internet
Cross-listed
cs.AI
Citations
185
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this approach provides important operational advantages with respect to traditional optimization algorithms.
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