Intelligent load balancing in SDN

Varalakshmi, P.; Mithesh, A; Niveditha, B; G, Rubak Preyan; Yogeeswar, S

doi:10.1109/icaccs54159.2022.9785334

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…Singh et al [4] designed the round robin algorithm to balance the load between SDN controller servers. To obtain better results in terms of latency, network speed, fault tolerance, and resource usage, Varalakshmi et al [5] designed a mechanism using round robin, randomized and weighted round robin, and minimum link algorithms to reduce the throughput of SDN controllers. Thajeer et al [6] used a hybrid algorithm as a server load balancing technique with a minimum link load balancing algorithm and weighted round robin load balancing algorithm, which flexibly balances the load among distributed SDN controllers.…”

Section: Related Workmentioning

confidence: 99%

Load balancing strategy for SDN multi-controller clusters based on load prediction

Xiao,

Pan,

Liu

et al. 2023

J Supercomput

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Software-defined networking (SDN) separates the control layer from the data layer, and decisions to manage the network are issued through a controller. The distributed SDN architecture is an effective solution addressing modern WAN SDN architectures and allows multiple controllers to manage different parts of the network to ensure efficient and stable operation. To solve the problems of high switch migration cost, load imbalance, and inefficient load balancing in SDN multi-controller environments, we propose a deep learning-based controller load prediction switch migration (LPSM) strategy that uses a migration switch selection algorithm, target controller selection algorithm, and switch migration decision algorithm. Then, we propose a load balancing algorithm based on this decision algorithm. The final experimental results show that the LPSM reduces the migration cost by 16% and 8%, respectively, compared with time-sharing switch migration (TSSM) and distributed decision migration (DDM) strategies, reduces load variance from 0.02 to 0.004 compared with the DDM strategy, and improves load balancing efficiency by 27.6% compared with the TSSM strategy.

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Section: Related Workmentioning

confidence: 99%

Load balancing strategy for SDN multi-controller clusters based on load prediction

Xiao,

Pan,

Liu

et al. 2023

J Supercomput

View full text Add to dashboard Cite

show abstract

Load Balancing Strategy for SDN Multi-controller ClustersBased on Load Prediction

Xiao

Pan

Liu

et al. 2023

Preprint

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Software-defined networking (SDN) separates the control layer from the data layer, and decisions tomanage the network are issued through a controller. The distributed SDN architecture is an effectivesolution addressing modern WAN SDN architectures and allows multiple controllers to managedifferent parts of the network to ensure efficient and stable operation. To solve the problems of highswitch migration cost, load imbalance, and inefficient load balancing in SDN multi-controller environments,we propose a deep learning-based controller load prediction switch migration (LPSM) strategythat uses a migration switch selection algorithm, target controller selection algorithm, and switchmigration decision algorithm. Then, we propose a load balancing algorithm based on this decisionalgorithm. The final experimental results show that the LPSM reduces the migration cost by 16%and 8%, respectively, compared with time-sharing switch migration (TSSM) and distributed decisionmigration (DDM) strategies, reduces load variance from 0.02 to 0.004 compared with the DDMstrategy, and improves load balancing efficiency by 27.6% compared with the TSSM strategy.

show abstract

Intelligent load balancing in SDN

Cited by 2 publications

References 2 publications

Load balancing strategy for SDN multi-controller clusters based on load prediction

Load balancing strategy for SDN multi-controller clusters based on load prediction

Load Balancing Strategy for SDN Multi-controller ClustersBased on Load Prediction

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