{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:44:39Z","timestamp":1774539879389,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Islamic Azad University, Iran, Islamic Republic Of mahabad"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Internet Things"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>In prospective advancements, it is anticipated that software-defined networking (SDN) will establish itself as the principal framework for the implementation of heterogeneous networks. Unlike traditional networking paradigms, SDN differentiates the control and data planes, thereby enabling enhanced routing and management of traffic across various domains. The controllers located within the control plane are responsible for programming the forwarding devices situated in the data plane, whereas the highest layer, referred to as the application plane, enforces regulatory policies and coordinates network programming. Interfacing mechanisms are utilized across the different layers of SDN to ensure effective communication. Nonetheless, SDN faces challenges related to traffic distribution, such as load imbalance, which could negatively affect overall network performance. In response, developers have formulated a range of SDN load-balancing solutions intended to improve the efficiency of SDN. Furthermore, owing to the swift advancements in the domain of artificial intelligence (AI), researchers are investigating the viability of incorporating AI methodologies into SDN to optimize resource utilization and enhance overall performance. This survey is organized as follows: First, it provides an analysis of the SDN architecture and investigates the load balancing challenges present within SDN. Second, it classifies AI-driven load balancing strategies and critically assesses these mechanisms from multiple perspectives, including the algorithms\/techniques utilized, the particular issues addressed, and their corresponding advantages and disadvantages. Third, it consolidates the metrics used to evaluate the effectiveness of these techniques. Finally, it identifies the emerging trends and challenges associated with AI-enhanced load balancing for future research endeavors.<\/jats:p>","DOI":"10.1007\/s43926-025-00098-5","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T02:17:35Z","timestamp":1737339455000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A comprehensive overview of load balancing methods in software-defined networks"],"prefix":"10.1007","volume":"5","author":[{"given":"Rasoul","family":"Farahi","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"key":"98_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108047","volume":"192","author":"M Priyadarsini","year":"2021","unstructured":"Priyadarsini M, Bera P. Software defined networking architecture, traffic management, security, and placement: a survey. Comput Netw. 2021;192: 108047.","journal-title":"Comput Netw"},{"key":"98_CR2","unstructured":"Zhu, L., Karim, M. M., Sharif, K., Li, F., Du, X., & Guizani, M. (2019). SDN controllers: Benchmarking & performance evaluation. arXiv preprint arXiv:1902.04491."},{"key":"98_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102856","volume":"174","author":"M Hamdan","year":"2021","unstructured":"Hamdan M, Hassan E, Abdelaziz A, Elhigazi A, Mohammed B, Khan S, Marsono MN. A comprehensive survey of load balancing techniques in software-defined network. J Netw Comput Appl. 2021;174: 102856.","journal-title":"J Netw Comput Appl"},{"issue":"4","key":"98_CR4","doi-asserted-by":"publisher","first-page":"2181","DOI":"10.1109\/COMST.2014.2326417","volume":"16","author":"F Hu","year":"2014","unstructured":"Hu F, Hao Q, Bao K. A survey on software-defined network and openflow: From concept to implementation. IEEE Commun Surv Tutorials. 2014;16(4):2181\u2013206.","journal-title":"IEEE Commun Surv Tutorials"},{"issue":"2","key":"98_CR5","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1049\/iet-net.2018.5082","volume":"8","author":"M Latah","year":"2019","unstructured":"Latah M, Toker L. Artificial intelligence enabled software-defined networking: a comprehensive overview. IET networks. 2019;8(2):79\u201399.","journal-title":"IET networks"},{"key":"98_CR6","doi-asserted-by":"crossref","unstructured":"Shang, Z., Chen, W., Ma, Q., & Wu, B. (2013, November). Design and implementation of server cluster dynamic load balancing based on OpenFlow. In 2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST 2013 & UMEDIA 2013) (pp. 691\u2013697). IEEE.","DOI":"10.1109\/ICAwST.2013.6765526"},{"key":"98_CR7","doi-asserted-by":"crossref","unstructured":"Leland, R., & Hendrickson, B. (1994, May). An empirical study of static load balancing algorithms. In Proceedings of IEEE Scalable High Performance Computing Conference (pp. 682\u2013685). IEEE.","DOI":"10.1109\/SHPCC.1994.296707"},{"issue":"5","key":"98_CR8","doi-asserted-by":"publisher","first-page":"2292","DOI":"10.1016\/j.asoc.2013.01.025","volume":"13","author":"LD Dhinesh Babu","year":"2013","unstructured":"Dhinesh Babu LD, Venkata Krishna P. Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput. 2013;13(5):2292\u2013303.","journal-title":"Appl Soft Comput"},{"issue":"8","key":"98_CR9","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1109\/MCOM.2018.1701016","volume":"56","author":"AA Abdelltif","year":"2018","unstructured":"Abdelltif AA, Ahmed E, Fong AT, Gani A, Imran M. SDN-based load balancing service for cloud servers. IEEE Commun Mag. 2018;56(8):106\u201311.","journal-title":"IEEE Commun Mag"},{"issue":"80","key":"98_CR10","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.future.2017.11.012","volume":"1","author":"H Zhong","year":"2018","unstructured":"Zhong H, Fang Y, Cui J. Reprint of \u201cLBBSRT: An efficient SDN load balancing scheme based on server response time.\u201d Futur Gener Comput Syst. 2018;1(80):409\u201316.","journal-title":"Futur Gener Comput Syst"},{"key":"98_CR11","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1007\/s11277-020-07130-7","volume":"112","author":"V Srivastava","year":"2020","unstructured":"Srivastava V, Pandey RS. A dominance of the channel capacity in load balancing of software defined network. Wireless Pers Commun. 2020;112:1859\u201373.","journal-title":"Wireless Pers Commun"},{"issue":"4","key":"98_CR12","doi-asserted-by":"publisher","first-page":"5583","DOI":"10.1109\/JSYST.2020.3009315","volume":"15","author":"X Chen","year":"2020","unstructured":"Chen X, Wang X, Yi B, He Q, Huang M. Deep learning-based traffic prediction for energy efficiency optimization in software-defined networking. IEEE Syst J. 2020;15(4):5583\u201394.","journal-title":"IEEE Syst J"},{"issue":"1","key":"98_CR13","doi-asserted-by":"publisher","first-page":"12","DOI":"10.23919\/JCN.2021.000004","volume":"23","author":"TVT Duong","year":"2021","unstructured":"Duong TVT. Load balancing routing under constraints of quality of transmission in mesh wireless network based on software defined networking. J Commun Netw. 2021;23(1):12\u201322.","journal-title":"J Commun Netw"},{"key":"98_CR14","doi-asserted-by":"publisher","first-page":"93187","DOI":"10.1109\/ACCESS.2020.2994912","volume":"8","author":"X Han","year":"2020","unstructured":"Han X, Meng X, Yu Z, Kang Q, Zhao Y. A service function chain deployment method based on network flow theory for load balance in operator networks. IEEE Access. 2020;8:93187\u201399.","journal-title":"IEEE Access"},{"key":"98_CR15","doi-asserted-by":"crossref","unstructured":"Andjamba, T. S., & Zodi, G. A. L. (2023, January). A load balancing protocol for improved video on demand in sdn-based clouds. In 2023 17th international conference on ubiquitous information management and communication (IMCOM) (pp. 1\u20136). IEEE.","DOI":"10.1109\/IMCOM56909.2023.10035591"},{"issue":"1","key":"98_CR16","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s12243-022-00921-y","volume":"78","author":"J Gal\u00e1n-Jim\u00e9nez","year":"2023","unstructured":"Gal\u00e1n-Jim\u00e9nez J, Polverini M, Lavacca FG, Herrera JL, Berrocal J. Joint energy efficiency and load balancing optimization in hybrid IP\/SDN networks. Ann Telecommun. 2023;78(1):13\u201331.","journal-title":"Ann Telecommun"},{"key":"98_CR17","doi-asserted-by":"crossref","unstructured":"Abhishek, P. M., Naik, A., Doddannavar, P., Patil, R., Raikar, M. M., & Meena, S. M. (2022). Load Balancing for Network Resource Management in Software-Defined Networks. In Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2022 (pp. 193\u2013203). Singapore: Springer Nature Singapore.","DOI":"10.1007\/978-981-19-1018-0_17"},{"issue":"7","key":"98_CR18","doi-asserted-by":"publisher","first-page":"7438","DOI":"10.1007\/s11227-022-04957-8","volume":"79","author":"H Zheng","year":"2023","unstructured":"Zheng H, Guo J, Zhou Q, Peng Y, Chen Y. Application of improved ant colony algorithm in load balancing of software-defined networks. J Supercomput. 2023;79(7):7438\u201360.","journal-title":"J Supercomput"},{"issue":"4","key":"98_CR19","doi-asserted-by":"publisher","first-page":"3323","DOI":"10.1109\/JIOT.2020.2967081","volume":"7","author":"A Montazerolghaem","year":"2020","unstructured":"Montazerolghaem A, Yaghmaee MH. Load-balanced and QoS-aware software-defined Internet of Things. IEEE Internet Things J. 2020;7(4):3323\u201337.","journal-title":"IEEE Internet Things J"},{"key":"98_CR20","doi-asserted-by":"crossref","unstructured":"Hassas Yeganeh, S., & Ganjali, Y. (2012, August). Kandoo: a framework for efficient and scalable offloading of control applications. In Proceedings of the first workshop on Hot topics in software defined networks (pp. 19\u201324).","DOI":"10.1145\/2342441.2342446"},{"key":"98_CR21","doi-asserted-by":"publisher","first-page":"46646","DOI":"10.1109\/ACCESS.2019.2909356","volume":"7","author":"S Ejaz","year":"2019","unstructured":"Ejaz S, Iqbal Z, Shah PA, Bukhari BH, Ali A, Aadil F. Traffic load balancing using software defined networking (SDN) controller as virtualized network function. IEEE Access. 2019;7:46646\u201358.","journal-title":"IEEE Access"},{"key":"98_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, L., Shou, G., Hu, Y., & Guo, Z. (2013, November). Deployment of intrusion prevention system based on software defined networking. In 2013 15th IEEE International Conference on Communication Technology (pp. 26\u201331). IEEE.","DOI":"10.1109\/ICCT.2013.6820345"},{"key":"98_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108369","volume":"198","author":"H Mokhtar","year":"2021","unstructured":"Mokhtar H, Di X, Zhou Y, Hassan A, Ma Z, Musa S. Multiple-level threshold load balancing in distributed SDN controllers. Comput Netw. 2021;198: 108369.","journal-title":"Comput Netw"},{"key":"98_CR24","volume":"2","author":"M Xiang","year":"2022","unstructured":"Xiang M, Chen M, Wang D, Luo Z. Deep reinforcement learning-based load balancing strategy for multiple controllers in sdn e-prime-advances in electrical engineering. Electron Energy. 2022;2: 100038.","journal-title":"Electron Energy"},{"issue":"7","key":"98_CR25","doi-asserted-by":"publisher","first-page":"5852","DOI":"10.1109\/JIOT.2019.2952527","volume":"7","author":"KS Sahoo","year":"2019","unstructured":"Sahoo KS, Puthal D, Tiwary M, Usman M, Sahoo B, Wen Z, Ranjan R. ESMLB Efficient switch migration-based load balancing for multicontroller SDN in IoT. IEEE Int Things J. 2019;7(7):5852\u201360.","journal-title":"IEEE Int Things J"},{"key":"98_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108749","volume":"205","author":"H Zhong","year":"2022","unstructured":"Zhong H, Xu J, Cui J, Sun X, Gu C, Liu L. Prediction-based dual-weight switch migration scheme for SDN load balancing. Comput Netw. 2022;205: 108749.","journal-title":"Comput Netw"},{"key":"98_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.109145","volume":"214","author":"S Zafar","year":"2022","unstructured":"Zafar S, Lv Z, Zaydi NH, Ibrar M, Hu X. DSMLB: Dynamic switch-migration based load balancing for software-defined IoT network. Comput Netw. 2022;214: 109145.","journal-title":"Comput Netw"},{"key":"98_CR28","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.comnet.2013.12.004","volume":"68","author":"Z Guo","year":"2014","unstructured":"Guo Z, Su M, Xu Y, Duan Z, Wang L, Hui S, Chao HJ. Improving the performance of load balancing in software-defined networks through load variance-based synchronization. Comput Netw. 2014;68:95\u2013109.","journal-title":"Comput Netw"},{"key":"98_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108330","volume":"241","author":"C Li","year":"2022","unstructured":"Li C, Jiang K, Luo Y. Dynamic placement of multiple controllers based on SDN and allocation of computational resources based on heuristic ant colony algorithm. Knowl-Based Syst. 2022;241: 108330.","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"98_CR30","doi-asserted-by":"publisher","first-page":"1386","DOI":"10.1109\/TGCN.2022.3162237","volume":"6","author":"H Babbar","year":"2022","unstructured":"Babbar H, Rani S, Bashir AK, Nawaz R. LBSMT: Load balancing switch migration algorithm for cooperative communication intelligent transportation systems. IEEE Transact Green Commun Netw. 2022;6(3):1386\u201395.","journal-title":"IEEE Transact Green Commun Netw"},{"key":"98_CR31","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.comnet.2018.05.012","volume":"141","author":"H Wang","year":"2018","unstructured":"Wang H, Xu H, Huang L, Wang J, Yang X. Load-balancing routing in software defined networks with multiple controllers. Comput Netw. 2018;141:82\u201391.","journal-title":"Comput Netw"},{"key":"98_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107230","volume":"177","author":"P Sun","year":"2020","unstructured":"Sun P, Guo Z, Wang G, Lan J, Hu Y. MARVEL: Enabling controller load balancing in software-defined networks with multi-agent reinforcement learning. Comput Netw. 2020;177: 107230.","journal-title":"Comput Netw"},{"issue":"4","key":"98_CR33","doi-asserted-by":"publisher","first-page":"3133","DOI":"10.1109\/COMST.2019.2916583","volume":"21","author":"NC Luong","year":"2019","unstructured":"Luong NC, Hoang DT, Gong S, Niyato D, Wang P, Liang YC, Kim DI. Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun Surv Tutorials. 2019;21(4):3133\u201374.","journal-title":"IEEE Commun Surv Tutorials"},{"issue":"12","key":"98_CR34","doi-asserted-by":"publisher","first-page":"15947","DOI":"10.1109\/TVT.2020.3038918","volume":"69","author":"A Filali","year":"2020","unstructured":"Filali A, Mlika Z, Cherkaoui S, Kobbane A. Preemptive SDN load balancing with machine learning for latency sensitive applications. IEEE Trans Veh Technol. 2020;69(12):15947\u201363.","journal-title":"IEEE Trans Veh Technol"},{"issue":"2","key":"98_CR35","doi-asserted-by":"publisher","first-page":"162","DOI":"10.3390\/electronics10020162","volume":"10","author":"S Yeo","year":"2021","unstructured":"Yeo S, Naing Y, Kim T, Oh S. Achieving balanced load distribution with reinforcement learning-based switch migration in distributed SDN controllers. Electronics. 2021;10(2):162.","journal-title":"Electronics"},{"key":"98_CR36","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhou, X., Gao, J., & Qin, Y. (2018, November). SDN controller load balancing based on reinforcement learning. In 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS) (pp. 1120\u20131126). IEEE.","DOI":"10.1109\/ICSESS.2018.8663757"},{"issue":"10","key":"98_CR37","doi-asserted-by":"publisher","first-page":"2249","DOI":"10.1109\/JSAC.2020.3000371","volume":"38","author":"J Zhang","year":"2020","unstructured":"Zhang J, Ye M, Guo Z, Yen CY, Chao HJ. CFR-RL: Traffic engineering with reinforcement learning in SDN. IEEE J Sel Areas Commun. 2020;38(10):2249\u201359.","journal-title":"IEEE J Sel Areas Commun"},{"key":"98_CR38","doi-asserted-by":"crossref","unstructured":"Sun P, Lan J, Guo Z, Xu Y, Hu Y. Improving the scalability of deep reinforcement learning-based routing with control on partial nodes. InICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 May 4 (pp. 3557\u20133561). IEEE.","DOI":"10.1109\/ICASSP40776.2020.9054483"},{"key":"98_CR39","doi-asserted-by":"crossref","unstructured":"Rupani K, Punjabi N, Shamdasani M, Chaudhari S. Dynamic load balancing in software-defined networks using. InProceeding of International Conference on Computational Science and Applications: ICCSA 2019 (p. 283).","DOI":"10.1007\/978-981-15-0790-8_28"},{"key":"98_CR40","unstructured":"Michael, N. (2005). Artificial intelligence a guide to intelligent systems."},{"key":"98_CR41","volume-title":"International symposium on experimental algorithms","author":"XS Yang","year":"2011","unstructured":"Yang XS. Metaheuristic optimization: algorithm analysis and open problems. In: International symposium on experimental algorithms. Cham: Springer; 2011."},{"issue":"2\u20133","key":"98_CR42","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.cie.2004.07.001","volume":"47","author":"MA El-Baz","year":"2004","unstructured":"El-Baz MA. A genetic algorithm for facility layout problems of different manufacturing environments. Comput Ind Eng. 2004;47(2\u20133):233\u201346.","journal-title":"Comput Ind Eng"},{"key":"98_CR43","unstructured":"Sanner, J. M., Ouzzif, M., Hadjadj-Aoul, Y., & Dartois, J. E. (2016, November). Evolutionary algorithms for optimized SDN controllers & NVFs\u2019 placement in SDN networks. In SDN Day 2016."},{"key":"98_CR44","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s11721-007-0002-0","volume":"1","author":"R Poli","year":"2007","unstructured":"Poli R, Kennedy J, Blackwell T. Particle swarm optimization: an overview. Swarm Intell. 2007;1:33\u201357.","journal-title":"Swarm Intell"},{"key":"98_CR45","doi-asserted-by":"crossref","unstructured":"Gao, C., Wang, H., Zhu, F., Zhai, L., & Yi, S. (2015). A particle swarm optimization algorithm for controller placement problem in software defined network. In Algorithms and Architectures for Parallel Processing: 15th International Conference, ICA3PP 2015, Zhangjiajie, China, November 18\u201320, 2015, Proceedings, Part III 15 (pp. 44\u201354). Springer International Publishing.","DOI":"10.1007\/978-3-319-27137-8_4"},{"issue":"6","key":"98_CR46","doi-asserted-by":"publisher","first-page":"2533","DOI":"10.1007\/s11276-022-02990-2","volume":"28","author":"Z Kabiri","year":"2022","unstructured":"Kabiri Z, Barekatain B, Avokh A. GOP-SDN: an enhanced load balancing method based on genetic and optimized particle swarm optimization algorithm in distributed SDNs. Wireless Netw. 2022;28(6):2533\u201352.","journal-title":"Wireless Netw"},{"issue":"1","key":"98_CR47","doi-asserted-by":"publisher","first-page":"247","DOI":"10.3233\/JIFS-179399","volume":"38","author":"H Li","year":"2020","unstructured":"Li H, Lu H, Fu X. An optimal and dynamic elephant flow scheduling for SDN-based data center networks. J Intell Fuzzy Syst. 2020;38(1):247\u201355.","journal-title":"J Intell Fuzzy Syst"},{"issue":"2","key":"98_CR48","doi-asserted-by":"publisher","first-page":"311","DOI":"10.3390\/s19020311","volume":"19","author":"H Xue","year":"2019","unstructured":"Xue H, Kim KT, Youn HY. Dynamic load balancing of software-defined networking based on genetic-ant colony optimization. Sensors. 2019;19(2):311.","journal-title":"Sensors"},{"issue":"6","key":"98_CR49","doi-asserted-by":"publisher","first-page":"700","DOI":"10.3390\/electronics10060700","volume":"10","author":"A Guo","year":"2021","unstructured":"Guo A, Yuan C. Network intelligent control and traffic optimization based on SDN and artificial intelligence. Electronics. 2021;10(6):700.","journal-title":"Electronics"},{"issue":"16","key":"98_CR50","doi-asserted-by":"publisher","first-page":"3741","DOI":"10.1016\/j.comcom.2008.05.019","volume":"31","author":"RG Garroppo","year":"2008","unstructured":"Garroppo RG, Giordano S, Pagano M, Procissi G. On traffic prediction for resource allocation: a Chebyshev bound based allocation scheme. Comput Commun. 2008;31(16):3741\u201351.","journal-title":"Comput Commun"},{"issue":"3","key":"98_CR51","doi-asserted-by":"publisher","first-page":"2432","DOI":"10.1109\/JIOT.2021.3095237","volume":"9","author":"A Montazerolghaem","year":"2021","unstructured":"Montazerolghaem A. Software-defined Internet of Multimedia Things: Energy-efficient and Load-balanced Resource Management. IEEE Internet Things J. 2021;9(3):2432\u201342.","journal-title":"IEEE Internet Things J"},{"key":"98_CR52","volume-title":"Encyclopedia of Statistical Sciences","author":"GM Jenkins","year":"2004","unstructured":"Jenkins GM. Autoregressive-Integrated Moving Average (ARIMA) Models. In: Encyclopedia of Statistical Sciences. Hoboken: Wiley; 2004."},{"issue":"8","key":"98_CR53","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380.","journal-title":"Neural Comput"},{"key":"98_CR54","doi-asserted-by":"crossref","unstructured":"Li L, Xu Q. Load balancing researches in SDN: A survey. In2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC) 2017 Jul 21 (pp. 403\u2013408). IEEE.","DOI":"10.1109\/ICEIEC.2017.8076592"},{"issue":"2","key":"98_CR55","first-page":"245","volume":"14","author":"M Mehra","year":"2019","unstructured":"Mehra M, Maurya S, Tiwari NK. Network load balancing in software defined network: a survey. Int J Appl Eng Res. 2019;14(2):245\u201353.","journal-title":"Int J Appl Eng Res"},{"key":"98_CR56","doi-asserted-by":"publisher","first-page":"98612","DOI":"10.1109\/ACCESS.2020.2995849","volume":"8","author":"MR Belgaum","year":"2020","unstructured":"Belgaum MR, Musa S, Alam MM. A systematic review of load balancing techniques in software-defined networking. IEEE Access. 2020;8:98612\u201336.","journal-title":"IEEE Access."},{"key":"98_CR57","doi-asserted-by":"publisher","DOI":"10.3260\/cmc.2022.018211","author":"MR Belgaum","year":"2022","unstructured":"Belgaum MR, Ali F, Alansari Z, Musa S, Alam MM, Mazliham MS. Artificial intelligence based reliable load balancing framework in software-defined networks. Comput Mater Contin. 2022. https:\/\/doi.org\/10.3260\/cmc.2022.018211.","journal-title":"Comput Mater Contin"}],"container-title":["Discover Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-025-00098-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43926-025-00098-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-025-00098-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T03:07:21Z","timestamp":1737342441000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43926-025-00098-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,20]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["98"],"URL":"https:\/\/doi.org\/10.1007\/s43926-025-00098-5","relation":{},"ISSN":["2730-7239"],"issn-type":[{"value":"2730-7239","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,20]]},"assertion":[{"value":"3 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"6"}}