Traffic engineering using new VS routing scheme

Saraph, G.P.; Singh, P.

doi:10.1109/icc.2004.1312697

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This modification targeted at Publish/Subscribe (Pub/Sub) paradigms preserves the Pub/Sub bandwidth with less computational resources. In order to adapt VS-routing [67] optimization to SDN networks, [66] proposed a network hop count technique to improve the reward function of SARSA algorithm. The VS-routing introduces an − function in the network hop count which is calculated to select the optimal route and avoid the long package queue of network links in the SDN architecture.…”

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%