CoDRL: Intelligent Packet Routing in SDN Using Convolutional Deep Reinforcement Learning
| Autor: | Uttam Kamalia, Tejas Modi, Pravati Swain, Raj Bhandarkar |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 05 social sciences Network delay Q-learning 050801 communication & media studies 020206 networking & telecommunications 02 engineering and technology Network congestion 0508 media and communications Convergence (routing) 0202 electrical engineering electronic engineering information engineering Reinforcement learning Network performance Routing (electronic design automation) Software-defined networking business Computer network |
| Zdroj: | ANTS |
| Popis: | Software Defined Networking (SDN) provides opportunities for flexible and dynamic traffic engineering. However, in current SDN systems, routing strategies are based on traditional mechanisms which lack in real-time modification and less efficient resource utilization. To overcome these limitations, deep learning is used in this paper to improve the routing computation in SDN. This paper proposes Convolutional Deep Reinforcement Learning (CoDRL) model which is based on deep reinforcement learning agent for routing optimization in SDN to minimize the mean network delay and packet loss rate. The CoDRL model consists of Deep Deterministic Policy Gradients (DDPG) deep agent coupled with Convolution layer. The proposed model tends to automatically adapts the dynamic packet routing using network data obtained through the SDN controller, and provides the routing configuration that attempts to reduce network congestion and minimize the mean network delay. Hence, the proposed deep agent exhibits good convergence towards providing routing configurations that improves the network performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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