Abstract
With the exponential increase in connected devices and its accompanying complexities in network management, dynamic Traffic Engineering (TE) solutions in Software-Defined Networking (SDN) using Reinforcement Learning (RL) techniques has emerged in recent times. The SDN architecture empowers network operators to monitor network traffic with agility, flexibility, robustness and centralized control. The separation of the control and the forwarding plane in SDN has enabled the integration of RL agents in the networking architecture to enforce changes in traffic patterns during network congestions. This paper surveys major RL techniques adopted for efficient TE in SDN. We reviewed the use of RL agents in modelling TE policies for SDNs, with agents’ actions on the environment guided by future rewards and a new state. We further looked at the SARL and MARL algorithms the RL agents deploy in forming policies for the environment. The paper finally looked at agents design architecture in SDN and possible research gaps.
Highlights
The emergence of fifth generation (5G) networks has propelled the growth of Internet of Things (IoT) in recent times
For intelligent routing in softwaredefined data-centers (SD-DCN), [89] proposed a deep reinforcement learning based routing (DRL-R) consisting of Deep Deterministic Policy Gradient (DDPG)-Deep Q-Network (DQN) agent to perform a reasonable routing adapted to the network state
The Reinforcement Learning (RL) agent is embedded in the AI Plane and uses the DQN to learn the best optimal routing paths for the mice and elephant flows by obtaining the flow type, network state information and network performance evaluation from the control plane of the Software-Defined Networking (SDN) architecture
Summary
The emergence of fifth generation (5G) networks has propelled the growth of Internet of Things (IoT) in recent times. The elephant flows are heavy traffic flows that requires more network resources whiles the rapid aggregation of the mice flows can degrade the network. For TE, machine learning algorithms are adopted for intelligent flow re-routing with an efficient feature selection criterion [18] [19] in network flow analysis Deploying these machine learning algorithms in the SDN controller will efficiently allocate network resources and formulate policies for optimal network performance with low overheads. In this survey as outlined, we reviewed popular Reinforcement Learning (RL) techniques used in SDN architecture for Traffic Engineering with limitations on parameters chosen and approaches for future research.
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