An enhanced AHP–TOPSIS-based load balancing algorithm for switch migration in software-defined networks
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 migra...
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| Published in | The Journal of supercomputing Vol. 77; no. 1; pp. 563 - 596 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.01.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0920-8542 1573-0484 |
| DOI | 10.1007/s11227-020-03285-z |
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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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| AbstractList | 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. |
| Author | Ider, Masoud Barekatain, Behrang |
| Author_xml | – sequence: 1 givenname: Masoud surname: Ider fullname: Ider, Masoud organization: Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University – sequence: 2 givenname: Behrang surname: Barekatain fullname: Barekatain, Behrang email: Behrang_Barekatain@iaun.ac.ir organization: Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Big Data Research Center, Najafabad Branch, Islamic Azad University |
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| Keywords | Load balancing algorithm Throughput Software defined network (SDN) Response time AHP–TOPSIS Switch migration |
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| References_xml | – reference: Al-TamFCorreiaNOn load balancing via switch migration in software-defined networkingIEEE Access20197959989601010.1109/ACCESS.2019.2929651 – reference: LiGWangXZhangZSDN-based load balancing scheme for multi-controller deploymentIEEE Access20197396123962210.1109/ACCESS.2019.2906683 – reference: Hai NT, Kim D-S (2016) Efficient load balancing for multi-controller in SDN-based mission-critical networks. In: 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). IEEE, pp 420–425 – reference: MaY-WChenJ-LTsaiY-HChengK-HHungW-CLoad-balancing multiple controllers mechanism for software-defined networkingWirel Pers Commun20179443549357410.1007/s11277-016-3790-y – reference: ChenWShangZTianXLiHDynamic server cluster load balancing in virtualization environment with openflowInt J Distrib Sens Netw201511753153810.1155/2015/531538 – reference: XiaWWenYFohCHNiyatoDXieHA survey on software-defined networkingIEEE Commun Surv Tutor2014171275110.1109/COMST.2014.2330903 – reference: SahooKSSahooBCAMD: a switch migration based load balancing framework for software defined networksIET Netw2019826427110.1049/iet-net.2018.5166 – reference: Cello M, Xu Y, Walid A, Wilfong G, Chao HJ, Marchese M (2017) Balcon: a distributed elastic SDN control via efficient switch migration. In: 2017 IEEE International Conference on Cloud Engineering (IC2E). IEEE, pp 40–50 – reference: HuTYiPZhangJLanJA distributed decision mechanism for controller load balancing based on switch migration in SDNChina Commun2018151012914210.1109/CC.2018.8485475 – reference: Filali A, Kobbane A, Elmachkour M, Cherkaoui S (2018) SDN controller assignment and load balancing with minimum quota of processing capacity. In: 2018 IEEE International Conference on Communications (ICC). IEEE, pp 1–6 – reference: SangaiahAKSubramaniamPRZhengXA combined fuzzy DEMATEL and fuzzy TOPSIS approach for evaluating GSD project outcome factorsNeural Comput Appl20152651025104010.1007/s00521-014-1771-1 – reference: Dixit A, Hao F, Mukherjee S, Lakshman T, Kompella RR (2014) ElastiCon; an elastic distributed SDN controller. In: 2014 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS). IEEE, pp 17–27 – reference: CuiJLuQZhongHTianMLiuLA load-balancing mechanism for distributed SDN control plane using response timeIEEE Trans Netw Serv Manag20181541197120610.1109/TNSM.2018.2876369 – reference: LuJRuanDMulti-objective group decision making: methods, software and applications with fuzzy set techniques2007LondonImperial College Press10.1142/p505 – reference: HuTGuoZYiPBakerTLanJMulti-controller based software-defined networking: a surveyIEEE Access20186159801599610.1109/ACCESS.2018.2814738 – reference: KangS-BKwonG-ILoad balancing of software-defined network controller using genetic algorithmContemp Eng Sci201691888188810.12988/ces.2016.66105 – reference: Kasberg DW, Udapudi D, Yasser MA (2018) Automatic load balancing of switches in a cluster of controllers in a software-defined switch network. Google Patents – reference: Li Y, Pan D (2013) OpenFlow based load balancing for fat-tree networks with multipath support. In: Proceedings of the 12th IEEE International Conference on Communications (ICC’13), Budapest, Hungary, pp 1–5 – reference: Li J-Q, Sun E-C, Zhang Y-H (2018) Multi-threshold SDN controllers load balancing algorithm based on controller load. In: International Conference on Computer, Communication and Network Technology (CCNT 2018), Wuzhen, pp 1–10 – reference: Cbench benchmarking. https://githubcom/mininet/oflops/tree/master/cbench – reference: Kreutz D, Ramos F, Verissimo P, Rothenberg CE, Azodolmolky S, Uhlig S (2014) Software-defined networking: a comprehensive survey. arXiv preprint arXiv:14060440 – reference: Li L, Xu Q (2017) Load balancing researches in SDN: a survey. In: 2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC). IEEE, pp 403–408 – reference: SinghSJhaRKA survey on software defined networking: architecture for next generation networkJ Netw Syst Manag201725232137410.1007/s10922-016-9393-9 – reference: ZhangSLanJSunPJiangYOnline load balancing for distributed control plane in software-defined data center networkIEEE Access20186181841819110.1109/ACCESS.2018.2820148 – reference: XuYCelloMWangI-CWalidAWilfongGWenCH-PMarcheseMChaoHJDynamic switch migration in distributed software-defined networks to achieve controller load balanceIEEE J Sel Areas Commun201937351552910.1109/JSAC.2019.2894237 – reference: Wang K-Y, Kao S-J, Kao M-T (2018) An efficient load adjustment for balancing multiple controllers in reliable SDN systems. In: 2018 IEEE International Conference on Applied System Invention (ICASI). IEEE, pp 593–596 – reference: Cimorelli F, Priscoli FD, Pietrabissa A, Celsi LR, Suraci V, Zuccaro L (2016) A distributed load balancing algorithm for the control plane in software defined networking. In: 2016 24th Mediterranean Conference on Control and Automation (MED). IEEE, pp 1033–1040 – reference: Katta N, Hira M, Kim C, Sivaraman A, Rexford J (2016) Hula: scalable load balancing using programmable data planes. In: Proceedings of the Symposium on SDN Research. ACM, p 10 – reference: SangaiahAKGopalJBasuASubramaniamPRAn integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcomeNeural Comput Appl201728111112310.1007/s00521-015-2040-7 – reference: ChirammalHDMukhedkarPVettathuAMastering KVM virtualization2016BirminghamPackt Publishing Ltd – ident: 3285_CR9 doi: 10.1109/MED.2016.7535946 – volume-title: Multi-objective group decision making: methods, software and applications with fuzzy set techniques year: 2007 ident: 3285_CR14 doi: 10.1142/p505 – ident: 3285_CR13 doi: 10.1109/ICC.2018.8422750 – ident: #cr-split#-3285_CR18.2 – ident: 3285_CR8 doi: 10.1109/ICEIEC.2017.8076592 – volume: 28 start-page: 111 issue: 1 year: 2017 ident: 3285_CR15 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-2040-7 – ident: 3285_CR10 – volume: 6 start-page: 15980 year: 2018 ident: 3285_CR4 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2814738 – ident: 3285_CR25 doi: 10.1145/2890955.2890968 – volume: 37 start-page: 515 issue: 3 year: 2019 ident: 3285_CR23 publication-title: IEEE J Sel Areas Commun doi: 10.1109/JSAC.2019.2894237 – volume: 11 start-page: 531538 issue: 7 year: 2015 ident: 3285_CR26 publication-title: Int J Distrib Sens Netw doi: 10.1155/2015/531538 – volume: 26 start-page: 1025 issue: 5 year: 2015 ident: 3285_CR16 publication-title: Neural Comput Appl doi: 10.1007/s00521-014-1771-1 – ident: 3285_CR20 – ident: #cr-split#-3285_CR18.1 doi: 10.1145/2658260.2658261 – volume: 9 start-page: 881 issue: 18 year: 2016 ident: 3285_CR12 publication-title: Contemp Eng Sci doi: 10.12988/ces.2016.66105 – volume: 15 start-page: 129 issue: 10 year: 2018 ident: 3285_CR21 publication-title: China Commun doi: 10.1109/CC.2018.8485475 – volume: 25 start-page: 321 issue: 2 year: 2017 ident: 3285_CR2 publication-title: J Netw Syst Manag doi: 10.1007/s10922-016-9393-9 – ident: 3285_CR24 – ident: 3285_CR1 – volume: 94 start-page: 3549 issue: 4 year: 2017 ident: 3285_CR28 publication-title: Wirel Pers Commun doi: 10.1007/s11277-016-3790-y – volume: 8 start-page: 264 year: 2019 ident: 3285_CR6 publication-title: IET Netw doi: 10.1049/iet-net.2018.5166 – volume: 17 start-page: 27 issue: 1 year: 2014 ident: 3285_CR3 publication-title: IEEE Commun Surv Tutor doi: 10.1109/COMST.2014.2330903 – volume: 7 start-page: 95998 year: 2019 ident: 3285_CR7 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2929651 – volume: 15 start-page: 1197 issue: 4 year: 2018 ident: 3285_CR22 publication-title: IEEE Trans Netw Serv Manag doi: 10.1109/TNSM.2018.2876369 – ident: 3285_CR30 – volume: 7 start-page: 39612 year: 2019 ident: 3285_CR5 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2906683 – ident: 3285_CR27 – ident: 3285_CR19 doi: 10.1109/INDIN.2016.7819196 – volume: 6 start-page: 18184 year: 2018 ident: 3285_CR11 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2820148 – ident: 3285_CR17 doi: 10.1109/IC2E.2017.33 – volume-title: Mastering KVM virtualization year: 2016 ident: 3285_CR29 |
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| Title | An enhanced AHP–TOPSIS-based load balancing algorithm for switch migration in software-defined networks |
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