{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T02:51:03Z","timestamp":1769050263401,"version":"3.49.0"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T00:00:00Z","timestamp":1696204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T00:00:00Z","timestamp":1696204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Load balancing is major issue in federated cloud environment. Various services can be offered by different cloud service providers. As per current working environment cloud computing is used in major applications such as education, online shopping, multimedia services, etc. Dynamic load balancing is required to handle the resources. Federated cloud has various services offering system with computing resources, resource pooling, internet access services and storage. Intelligent Genetic algorithm is proposed to provide efficient load balancing service in hybrid cloud environment. Virtualized Intelligent Genetic Load Balancer algorithm consists of load balancer and resource provisioning system to allocate the resources. Enhanced Load Balancer is used to preserve the load and minimize the span time based on resource provisioning method. In this work we analyse automated virtual machine services by using runtime resource provision. Here we use enhanced load balancer to measure the performance using virtual machine placements, resource utilization and automated quality requirements. We design a deep belief network based on requirements and measure the accuracy using TensorFlow. The simulation results test the accuracy and compare the results. Virtualized Intelligent Genetic Load Balancer system is achieving the accuracy of 95% based on overall capacity requirements. We compare Virtualized Intelligent Genetic Load Balancer system performance with existing simulations results and compared the results.<\/jats:p>","DOI":"10.1186\/s13677-023-00514-7","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T13:02:14Z","timestamp":1696251734000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Virtualized intelligent genetic load balancer for federated hybrid cloud environment using deep belief network classifier"],"prefix":"10.1186","volume":"12","author":[{"given":"S.","family":"Rajkumar","sequence":"first","affiliation":[]},{"given":"Jeevaa","family":"Katiravan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,2]]},"reference":[{"issue":"10","key":"514_CR1","first-page":"2669","volume":"12","author":"S Manikandan","year":"2021","unstructured":"Manikandan S, Dhanalakshmi P, Priya S, Odilya Teen AM (2021) \u201cIntelligent and Deep Learning Collaborative method for E-Learning Educational Platform using TensorFlow.\u201d Turkish J Computer\u00a0 Mathematics Education 12(10):2669\u201376 (E-ISSN: 1309\u20134653, 2669\u20132676)","journal-title":"Turkish J Computer\u00a0 Mathematics Education"},{"issue":"3","key":"514_CR2","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.32604\/iasc.2022.022527","volume":"32","author":"S Manikandan","year":"2022","unstructured":"Manikandan S, Chinnadurai M (2022) Virtualized Load Balancer for Hybrid Cloud Using Genetic Algorithm. Intelligent Automation Soft Computing 32(3):1459\u20131466","journal-title":"Intelligent Automation Soft Computing"},{"issue":"3","key":"514_CR3","first-page":"2147","volume":"7","author":"S Manikandan","year":"2019","unstructured":"Manikandan S, Chinnadurai M (2019) 2019, \u2018Intelligent and Deep Learning Approach OT Measure E-Learning Content in Online Distance Education.\u2019 Online J Distance Educ e-Learning 7(3):2147\u20136454","journal-title":"Online J Distance Educ e-Learning"},{"key":"514_CR4","unstructured":"Anton Beloglazov and CanturkIsci, \u201cEfficient Resource Provisioning in Compute Clouds via VM Multiplexing\u201d IBM T. J. Watson Research Center Hawthorne, NY 10532, 2018"},{"issue":"12","key":"514_CR5","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1109\/TC.2010.90","volume":"59","author":"SO Luiz","year":"2010","unstructured":"Luiz SO, Perkusich A, Lima AMN (2010) Multisize Sliding Window in Workload Estimation for Dynamic Power Management. IEEE Trans Computers 59(12):1625\u20131639","journal-title":"IEEE Trans Computers"},{"key":"514_CR6","doi-asserted-by":"publisher","unstructured":"Chunfeng Lv,\u00a0Jianping Zhu\u00a0&\u00a0Zhengsu Tao, \u201cAn Improved Localization Scheme Based on PMCL Method for Large-Scale Mobile Wireless Aquaculture Sensor Networks\u201d, Arabian Journal for Science and Engineering\u00a0volume\u00a043,\u00a0pages1033\u20131052(2018), https:\/\/doi.org\/10.1007\/s13369-017-2871-x","DOI":"10.1007\/s13369-017-2871-x"},{"issue":"6","key":"514_CR7","doi-asserted-by":"publisher","first-page":"4496","DOI":"10.1109\/TIT.2018.2820686","volume":"64","author":"W Huang","year":"2018","unstructured":"Huang W, Ho T, Langberg M, Kliewer J (2018) Single-unicast secure network coding and network error correction are as hard as multiple-unicast network coding. IEEE Trans Inf Theory 64(6):4496\u20134512. https:\/\/doi.org\/10.1109\/TIT.2018.2820686","journal-title":"IEEE Trans Inf Theory"},{"key":"514_CR8","doi-asserted-by":"crossref","unstructured":"Rouvier M, Favre B (2016) SENSEI-LIF at SemEval-2016 Task 4 : Polarity embedding fusion for robust sentiment analysis. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). San Diego, Association for Computational Linguistics","DOI":"10.18653\/v1\/S16-1030"},{"key":"514_CR9","doi-asserted-by":"crossref","unstructured":"Cliche M, BB twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs, arXivPrepr. arXiv1704.06125","DOI":"10.18653\/v1\/S17-2094"},{"key":"514_CR10","doi-asserted-by":"crossref","unstructured":"Lei T, Joshi H, Barzilay R, Jaakkola T, Tymoshenko K, Moschitti A,\u00a0 Marquez L.\u00a0Semi-supervised Question Retrieval with Gated Convolutions. arXivPrepr. arXiv1512.05726, 2015","DOI":"10.18653\/v1\/N16-1153"},{"key":"514_CR11","doi-asserted-by":"crossref","unstructured":"Yin Y, Yangqiu S, eta Zhang M (2017) NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings. Proc 11th Int Work Semant Eval 621\u2013625","DOI":"10.18653\/v1\/S17-2102"},{"key":"514_CR12","doi-asserted-by":"crossref","unstructured":"Rodrigo N, Calheiros Rajiv Ranjan, Beloglazov Anton, De Rose C\u00e9sar A. F, Buyya Rajkumar (2015) CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms. Softw Pract Exp 41(1):23\u201350","DOI":"10.1002\/spe.995"},{"key":"514_CR13","unstructured":"Shinde V, Dange A, Lambay MA (2016) Load Balancing Algorithms in Cloud Computing.\u00a0Int J Comput\u00a0Sci\u00a0Trends\u00a0Technol\u00a0(IJCST)"},{"key":"514_CR14","doi-asserted-by":"crossref","unstructured":"Sekara K, Kosal KR (2017) SIQ Algorithm for Efficient Load Balancing In Cloud. IEEE 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET)","DOI":"10.1109\/ICAMMAET.2017.8186673"},{"key":"514_CR15","doi-asserted-by":"crossref","unstructured":"Dobber M, van der Mei R, Koole G (2016) Dynamic Load Balancing and Job Replication in a Global-Scale Grid Environment: A Comparison.\u00a0IEEE Trans Parallel Distrib Syst\u00a020(2):207","DOI":"10.1109\/TPDS.2008.61"},{"key":"514_CR16","unstructured":"Chaczko Z, Mahadevan V, Aslanzadeh S, Mcdermid C (2011) Availability and Load Balancing in Cloud Computing. 2011 International Conference on Computer and Software Modeling IPCSIT 14. IACSIT Press, Singapore"},{"key":"514_CR17","unstructured":"Christian S et al (2016) Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"514_CR18","volume-title":"On the Performance Variability of Production Cloud Services","author":"C Vazquez","year":"2019","unstructured":"Vazquez C (2019) On the Performance Variability of Production Cloud Services. Delft University of Technology, Parallel and Distributed Systems Group"},{"key":"514_CR19","unstructured":"Sulistio A (2020) Performance and Power Management for Cloud Infrastructures. Department of Mathematics and Computer Science, Distributed Systems Group"},{"key":"514_CR20","doi-asserted-by":"crossref","unstructured":"Priyanka CP, Subbiah S (2017) Comparative Analysis on Virtual Machine Assignment Algorithm. 2017 IEEE International Conference on Computing and Communication Technologies","DOI":"10.1109\/ICCCT2.2017.7972279"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-023-00514-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-023-00514-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-023-00514-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T13:44:07Z","timestamp":1700401447000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-023-00514-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,2]]},"references-count":20,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["514"],"URL":"https:\/\/doi.org\/10.1186\/s13677-023-00514-7","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,2]]},"assertion":[{"value":"8 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"138"}}