Dynamic Load Balancing of Multiple Controller based on Intelligent Collaboration in SDN

Yang, Shike; Shi, Haobin; Zhang, Hengsheng

doi:10.1109/cvidl51233.2020.00-72

Cited by 4 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For dynamic load balancing in multiple controllers due to switch migration conflicts, [62] proposed a SARSA-Bayesian RL algorithm for a multi-controller cluster design in SDN. With knowledge of the real-time load and controller's communication consumption, a request response model using the Bayesian [63] algorithm is combined with the SARSA RL mini-framework in a switch migration technique to the lighter controller.…”

Section: : End Loopmentioning

confidence: 99%

Traffic Engineering in Software-defined Networks using Reinforcement Learning: A Review

Dake¹,

Gadze²,

Klogo³

et al. 2021

IJACSA

View full text Add to dashboard Cite

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.

show abstract

Section: : End Loopmentioning

confidence: 99%