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Chaotic salp swarm algorithm for SDN multi-controller networks

Tutkimustuotosvertaisarvioitu

Standard

Chaotic salp swarm algorithm for SDN multi-controller networks. / Ateya, Abdelhamied A.; Muthanna, Ammar; Vybornova, Anastasia; Algarni, Abeer D.; Abuarqoub, Abdelrahman; Koucheryavy, Y.; Koucheryavy, Andrey.

julkaisussa: Engineering Science and Technology, Vuosikerta 22, Nro 4, 08.2019, s. 1001-1012.

Tutkimustuotosvertaisarvioitu

Harvard

Ateya, AA, Muthanna, A, Vybornova, A, Algarni, AD, Abuarqoub, A, Koucheryavy, Y & Koucheryavy, A 2019, 'Chaotic salp swarm algorithm for SDN multi-controller networks', Engineering Science and Technology, Vuosikerta. 22, Nro 4, Sivut 1001-1012. https://doi.org/10.1016/j.jestch.2018.12.015

APA

Ateya, A. A., Muthanna, A., Vybornova, A., Algarni, A. D., Abuarqoub, A., Koucheryavy, Y., & Koucheryavy, A. (2019). Chaotic salp swarm algorithm for SDN multi-controller networks. Engineering Science and Technology, 22(4), 1001-1012. https://doi.org/10.1016/j.jestch.2018.12.015

Vancouver

Ateya AA, Muthanna A, Vybornova A, Algarni AD, Abuarqoub A, Koucheryavy Y et al. Chaotic salp swarm algorithm for SDN multi-controller networks. Engineering Science and Technology. 2019 elo;22(4):1001-1012. https://doi.org/10.1016/j.jestch.2018.12.015

Author

Ateya, Abdelhamied A. ; Muthanna, Ammar ; Vybornova, Anastasia ; Algarni, Abeer D. ; Abuarqoub, Abdelrahman ; Koucheryavy, Y. ; Koucheryavy, Andrey. / Chaotic salp swarm algorithm for SDN multi-controller networks. Julkaisussa: Engineering Science and Technology. 2019 ; Vuosikerta 22, Nro 4. Sivut 1001-1012.

Bibtex - Lataa

@article{02a7f378dbd64cdb80082c3a1a504afb,
title = "Chaotic salp swarm algorithm for SDN multi-controller networks",
abstract = "Software-defined networking (SDN) is a novel network paradigm that enables flexible management for networks. However, with the increase in network capacity, a single controller of SDN has many limitations on both performance and scalability. Distributed multi-controller deployment is a promising method to satisfy fault tolerant and scalability. There are still open research issues related to controllers placement, and the optimal number of deployed controllers. In this paper, a dynamic optimization algorithm that is based on the Salp Swarm Optimization Algorithm (SSOA) is developed with the introduction of chaotic maps for enhancing the optimizer’s performance. The algorithm dynamically evaluates the optimum number of controllers and the optimal connections between switches and controllers in large scale SDN networks. In order to evaluate the proposed algorithm, several experiments were conducted and implemented in various scenarios. Moreover, the algorithm was compared to the linear and meta-heuristic algorithms. Simulation results show that the proposed algorithm outperforms meta-heuristic algorithms and a game theory based algorithm in terms of execution time and reliability.",
keywords = "Controller placement, Latency, Optimization algorithm, SDN, Swarm, Utilization",
author = "Ateya, {Abdelhamied A.} and Ammar Muthanna and Anastasia Vybornova and Algarni, {Abeer D.} and Abdelrahman Abuarqoub and Y. Koucheryavy and Andrey Koucheryavy",
year = "2019",
month = "8",
doi = "10.1016/j.jestch.2018.12.015",
language = "English",
volume = "22",
pages = "1001--1012",
journal = "Engineering Science and Technology",
issn = "2215-0986",
publisher = "Elsevier BV",
number = "4",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Chaotic salp swarm algorithm for SDN multi-controller networks

AU - Ateya, Abdelhamied A.

AU - Muthanna, Ammar

AU - Vybornova, Anastasia

AU - Algarni, Abeer D.

AU - Abuarqoub, Abdelrahman

AU - Koucheryavy, Y.

AU - Koucheryavy, Andrey

PY - 2019/8

Y1 - 2019/8

N2 - Software-defined networking (SDN) is a novel network paradigm that enables flexible management for networks. However, with the increase in network capacity, a single controller of SDN has many limitations on both performance and scalability. Distributed multi-controller deployment is a promising method to satisfy fault tolerant and scalability. There are still open research issues related to controllers placement, and the optimal number of deployed controllers. In this paper, a dynamic optimization algorithm that is based on the Salp Swarm Optimization Algorithm (SSOA) is developed with the introduction of chaotic maps for enhancing the optimizer’s performance. The algorithm dynamically evaluates the optimum number of controllers and the optimal connections between switches and controllers in large scale SDN networks. In order to evaluate the proposed algorithm, several experiments were conducted and implemented in various scenarios. Moreover, the algorithm was compared to the linear and meta-heuristic algorithms. Simulation results show that the proposed algorithm outperforms meta-heuristic algorithms and a game theory based algorithm in terms of execution time and reliability.

AB - Software-defined networking (SDN) is a novel network paradigm that enables flexible management for networks. However, with the increase in network capacity, a single controller of SDN has many limitations on both performance and scalability. Distributed multi-controller deployment is a promising method to satisfy fault tolerant and scalability. There are still open research issues related to controllers placement, and the optimal number of deployed controllers. In this paper, a dynamic optimization algorithm that is based on the Salp Swarm Optimization Algorithm (SSOA) is developed with the introduction of chaotic maps for enhancing the optimizer’s performance. The algorithm dynamically evaluates the optimum number of controllers and the optimal connections between switches and controllers in large scale SDN networks. In order to evaluate the proposed algorithm, several experiments were conducted and implemented in various scenarios. Moreover, the algorithm was compared to the linear and meta-heuristic algorithms. Simulation results show that the proposed algorithm outperforms meta-heuristic algorithms and a game theory based algorithm in terms of execution time and reliability.

KW - Controller placement

KW - Latency

KW - Optimization algorithm

KW - SDN

KW - Swarm

KW - Utilization

U2 - 10.1016/j.jestch.2018.12.015

DO - 10.1016/j.jestch.2018.12.015

M3 - Article

VL - 22

SP - 1001

EP - 1012

JO - Engineering Science and Technology

JF - Engineering Science and Technology

SN - 2215-0986

IS - 4

ER -