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Erschienen in: The Journal of Supercomputing 1/2021

21.04.2020

An enhanced AHP–TOPSIS-based load balancing algorithm for switch migration in software-defined networks

verfasst von: Masoud Ider, Behrang Barekatain

Erschienen in: The Journal of Supercomputing | Ausgabe 1/2021

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Abstract

Considering a software defined network, distributed controller architecture uses multiple controllers in which each controller manages a part of the network. The load imbalance problem in this architecture causes a large number of switch migrations resulting in a significant increase in switch migration cost and average network response time along with a decrease in throughput. Although recent studies have addressed these issues, access to optimal response time had been achieved with high cost of switch migration and sometimes with reduction of throughput using their methods. Therefore, the load balance in the present study is managed by a variable threshold based on the controllers’ workload. In other words, migration is done by selecting optimal switch and controller so that the switch will be selected with the lowest traffic generation rate which could return the source controller to its steady state. Using the suggested method, a destination controller is selected based on some important parameters such as CPU utilization, rate of incoming packets and the number of hops between switch and controller. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) algorithm is used to select the best controller based on the above-mentioned criteria and the AHP (analytic hierarchy process) algorithm is employed for determining the ratio of each criterion. The proposed method considerably outperforms other methods by achieving about 6 and 78% improvement in throughput and the number of switch migration in our implementation, respectively.

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Metadaten
Titel
An enhanced AHP–TOPSIS-based load balancing algorithm for switch migration in software-defined networks
verfasst von
Masoud Ider
Behrang Barekatain
Publikationsdatum
21.04.2020
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 1/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-020-03285-z

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