Q-learning Approach for Load-balancing in Software Defined Networks

Tennakoon, Deepal; Karunarathna, Suneth Namal; Udugama, Brian

doi:10.1109/mercon.2018.8421895

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…The optimal path chosen is delivered to the OpenVSwtiches (OVS) after Qrouting by the controller during network congestion. To balance the network load in SDN, [53] proposed a Q-learning approach to reduce the number of unsatisfied users in a 5G network architecture. The researchers used a flow admission control technique with a fairness function to enhance the perflow resource allocation in the network.…”

Section: : End Loopmentioning

confidence: 99%

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

Dake¹,

Gadze²,

Klogo³

et al. 2021

IJACSA

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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.

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Section: : End Loopmentioning

confidence: 99%

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

Dake¹,

Gadze²,

Klogo³

et al. 2021

IJACSA

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“…In the literature, there are only a few studies on AI-based SDN-load balancing. [18][19][20][21][22][23] These are as follows:…”

Section: Related Workmentioning

confidence: 99%

“…Tennakoon et al 20 propose a Q‐Learning approach for load balancing in SDN to reduce the number of unsatisfied users in the 5G network. The Q‐learning algorithm controls the flow of a user to the base station that gives the maximum reward rather than the minimum value for the fairness function.…”

Section: Related Workmentioning

confidence: 99%

Deep learning for load balancing of SDN‐based data center networks

Babayiğit

Ulu

2021

Int J Communication

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Summary With the development of new communication technologies, the amount of data transmission has increased gradually. To satisfy this increasing computing resource demand effectively, the number of data center networks (DCNs), which are structures composed of servers connected with well‐organized‐switches, has increased worldwide. However, traditional switches do not efficiently satisfy the needs of DCNs. In recent years, an emerging networking architecture software‐defined network (SDN) has been proposed to manage the DCNs to control network switches and to deploy new network protocols. However, the main challenge in DCNs is to balance the load among servers. One potential solution for this challenge is to use machine learning (ML) techniques to tackle network transmission demand. A recent successful ML technique is deep learning (DL) which makes prediction, classification, and decisions by handling large amounts of data. Although DL has drawn increasing attention in many research fields, its applications to networking problems are scarce. In this paper, a DL technique is proposed for the load‐balancing of SDN‐based DCNs. To train the DL network, the variable load values among links are used. The response time for load balancing of the DL technique is compared with those of different ML algorithms, such as an artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). The experimental results reveal that the response‐time results of ANN and DL are lower than those of the SVM and LR algorithms. Also, DL accuracy is higher than ANN accuracy. As a result, DL is very efficient for the load balancing of SDN‐based DCNs.

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