Combined optimization strategy: CUBW for load balancing in software defined network

Abstract

Software Defined Network (SDN) facilitates a centralized control management of devices in network, which solves many issues in the old network. However, as the modern era generates a vast amount of data, the controller in an SDN could become overloaded. Numerous investigators have offered their opinions on how to address the issue of controller overloading in order to resolve it. Mostly the traditional models consider two or three parameters to evenly distribute the load in SDN, which is not sufficient for precise load balancing strategy. Hence, an effective load balancing model is in need that considers different parameters. Considering this aspect, this paper presents a new load balancing model in SDN is introduced by following three major phases: (a) work load prediction, (b) optimal load balancing, and (c) switch migration. Initially, work load prediction is done via improved Deep Maxout Network. COA and BWO are conceptually combined in the proposed hybrid optimization technique known as Coati Updated Black Widow (CUBW). Then, the optimal load balancing is done via hybrid optimization named Coati Updated Black Widow (CUBW) Optimization Algorithm. The optimal load balancing is done by considering migration time, migration cost, distance and load balancing parameters like server load, response time and turnaround time. Finally, switch migration is carried out by considering the constraints like migration time, migration cost, and distance. The migration time of the proposed method achieves lower value, which is 27.3%, 40.8%, 24.40%, 41.8%, 42.8%, 42.2%, 40.0%, and 41.6% higher than the previous models like BMO, BES, AOA, TDO, CSO, GLSOM, HDD-PLB, BWO and COA respectively. Finally, the performance of proposed work is validated over the conventional methods in terms of different analysis.

Keywords

SDN load balancing deep Maxout CUBW switch migration

Get full access to this article

View all access options for this article.

References

Al-Tam and

Correia, On load balancing via switch migration in software-defined networking, IEEE Access 7 (2019). doi:10.1109/ACCESS.2019.2929651.

Banerjee and

D.M.

Akbar Hussain, EXPRL: Experience and prediction based load balancing strategy for multi-controller software defined networks, Int. j. inf. tecnol. 14 (2022), 2155–2169. doi:10.1007/s41870-019-00408-5.

Chahlaoui and

Dahmouni, A taxonomy of load balancing mechanisms in centralized and distributed SDN architectures, SN COMPUT. SCI. 1 (2020), 268. doi:10.1007/s42979-020-00288-8.

M.-L.

Chiang,

H.-S.

Cheng,

H.-Y.

Liu and

C.-Y.

Chiang, SDN-based server clusters with dynamic load balancing and performance improvement, Cluster Computing 24 (2021), 537–558. doi:10.1007/s10586-020-03135-w.

Dehghani,

Montazeri,

Trojovská and

Trojovský, Coati optimization algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems, Knowledge-Based Systems 259 (2023), 110011. doi:10.1016/j.knosys.2022.110011.

K.N.

Durai,

Subha and

Haldorai, Hybrid invasive weed improved grasshopper optimization algorithm for cloud load balancing, Intelligent Automation & Soft Computing 34(1) (2022).

Ejaz,

Iqbal,

P.A.

Shah,

B.H.

Bukhari,

Ali and

Aadil, Traffic load balancing using Software Defined Networking (SDN) controller as virtualized network function, IEEE Access 7 (2019), 04. doi:10.1109/ACCESS.2019.2909356.

El Kamel and

Youssef, Improving switch-to-controller assignment with load balancing in multi-controller software defined WAN (SD-WAN), Journal of Network and Systems Management 28 (2020), 553–575. doi:10.1007/s10922-020-09523-2.

Galán-Jiménez,

Polverini,

F.G.

Lavacca,

Luis Herrera and

Berrocal, Joint energy efficiency and load balancing optimization in hybrid IP/SDN networks, Annals of Telecommunications 78 (2023), 13–31. doi:10.1007/s12243-022-00921-y.

10.

Guo,

Dou,

Wang,

Liu,

Feng and

Xu, HybridFlow: Achieving load balancing in software-defined WANs with scalable routing, IEEE Transactions on Communications 69(8) (2021). doi:10.1109/TCOMM.2021.3074500.

11.

Hayyolalam and

Asghar Pourhaji Kazem, Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems, Engineering Applications of Artificial Intelligence 87 (2020). doi:10.1016/j.engappai.2019.103249.

12.

Ider and

Barekatain, An enhanced AHP–TOPSIS-based load balancing algorithm for switch migration in software-defined networks, The Journal of Supercomputing 77 (2021), 563–596. doi:10.1007/s11227-020-03285-z.

13.

Kaur and

Kaur, Load balancing optimization based on hybrid heuristic–metaheuristic techniques in cloud environment, Journal of King Saud University – Computer and Information Sciences 34 (2022).

14.

Kumar,

Venkanna and

Tiwari, Opt-ACM: An optimized load balancing based admission control mechanism for software defined hybrid wireless based IoT (SDHW-IoT) network, Computer Networks 188 (2021). doi:10.1016/j.comnet.2021.107888.

15.

Le and

T.-V.T.

Duong, Load balancing routing under constraints of quality of transmission in mesh wireless network based on software defined networking, JOURNAL OF COMMUNICATIONS AND NETWORKS 23(1) (2021). doi:10.23919/JCN.2021.000004.

16.

Mayilsamy and

D.P.

Rangasamy, Load balancing in software-defined networks using spider monkey optimization algorithm for the Internet of things, Wireless Personal Communications 116 (2021), 23–43. doi:10.1007/s11277-020-07703-6.

17.

Peta and

Koppu, An IoT-based framework and ensemble optimized deep maxout network model for breast cancer classification, Electronics 11 (2022), 4137. doi:10.3390/electronics11244137.

18.

Ping, Load balancing algorithms for big data flow classification based on heterogeneous computing in software definition networks, J Grid Computing 18 (2020), 275–291. doi:10.1007/s10723-020-09511-5.

19.

Priyadarsini,

Kumar,

Bera and

Ashiqur Rahman, An energy-efficient load distribution framework for SDN controllers, Computing 102 (2020), 2073–2098. doi:10.1007/s00607-019-00751-2.

20.

Ramya and

Ayothi, Hybrid dingo and whale optimization algorithm-based optimal load balancing for cloud computing environment, Transactions on Emerging Telecommunications Technologies 34(5) (2023), e4760.

21.

Sarbazi,

Sadeghzadeh and

S.J.

Mir Abedini, Improving resource allocation in software-defined networks using clustering, Cluster Computing 23 (2020), 1199–1210. doi:10.1007/s10586-019-02985-3.

22.

Shaban,

Maher,

Elbayoumi and

Abdelhady, A cuckoo load scheduling optimization approach for smart energy management, Energy Reports 7 (2021).

23.

Srisamarn,

Pradittasnee and

Kitsuwan, Resolving load imbalance state for SDN by minimizing maximum load of controllers, Journal of Network and Systems Management 29 (2021), 46. doi:10.1007/s10922-021-09612-w.

24.

Srivastava and

Shankar Pandey, A dominance of the channel capacity in load balancing of software defined network, Wireless Personal Communications 112 (2020). doi:10.1007/s11277-020-07130-7.

25.

Sulthana Begam,

Sangeetha and

N.R.

Shanker, Load balancing in DCN servers through SDN machine learning algorithm, Arabian Journal for Science and Engineering 47 (2022), 1423–1434. doi:10.1007/s13369-021-05911-1.

26.

Suna,

Guo,

Wang,

Lana and

Hu, MARVEL: Enabling controller load balancing in software-defined networks with multi-agent reinforcement learning, Computer Networks 177 (2020). doi:10.1016/j.comnet.2020.107230.

27.

Torkzadeh,

Soltanizadeh and

A.A.

Orouji, Energy-aware routing considering load balancing for SDN: A minimum graph-based ant colony optimization, Cluster Computing 24 (2021), 2293–2312. doi:10.1007/s10586-021-03263-x.

28.

Zhang,

Zeng and

Yi, Global load-balancing ONU assignment for a software-defined reconfigurable PON, Journal of Optical Communications and Networking 12 (2020), 7. doi:10.1364/JOCN.388134.