Routing protocol

Computer network

Economic growth

Link-state routing protocol

Routing domain

Reinforcement learning

Equal-cost multi-path routing

Distributed computing

Network packet

Emulation

Artificial intelligence

Economics

Policy-based routing

Static routing

Computer science

Quality of service

Routing (electronic design automation)

Packet loss

Multipath routing

Software-defined networking

Dynamic Source Routing

IEEE Transactions on Network and Service Management

Traditional routing protocols employ limited information to make routing decisions, which can lead to a slow adaptation to traffic variability, as well as restricted support to the Quality of Service (QoS) requirements of applications. This article introduces a novel approach for routing in Software-defined networking (SDN), called Reinforcement Learning and Software-Defined Networking Intelligent Routing (RSIR). RSIR adds a Knowledge Plane to SDN and defines a routing algorithm based on Reinforcement Learning (RL) that takes into account link-state information to make routing decisions. This algorithm capitalizes on the interaction with the environment, the intelligence provided by RL and the global view and control of the network furnished by SDN, to compute and install, in advance, optimal routes in the forwarding devices. RSIR was extensively evaluated by emulation using real traffic matrices. Results show RSIR outperforms the Dijkstra's algorithm in relation to the stretch, link throughput, packet loss, and delay when available bandwidth, delay, and loss are considered individually or jointly for the computation of optimal paths. The results demonstrate that RSIR is an attractive solution for intelligent routing in SDN.

Intelligent Routing Based on Reinforcement Learning for Software-Defined Networking