{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:56:33Z","timestamp":1775145393896,"version":"3.50.1"},"reference-count":40,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T00:00:00Z","timestamp":1593734400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Cloud computing deals with voluminous heterogeneous data, and there is a need to effectively distribute the load across clusters of nodes to achieve optimal performance in terms of resource usage, throughput, response time, reliability, fault tolerance, and so on. The swarm intelligence methodologies use artificial intelligence to solve computationally challenging problems like load balancing, scheduling, and resource allocation at finite time intervals. In literature, sufficient works are being carried out to address load balancing problem in the cloud using traditional swarm intelligence techniques like ant colony optimization, particle swarm optimization, cuckoo search, bat optimization, and so on. But the traditional swarm intelligence techniques have issues with respect to convergence rate, arriving at the global optimum solution, complexity in implementation and scalability, which limits the applicability of such techniques in cloud domain. In this paper, we look into performance modeling aspects of some of the recent competitive swarm artificial intelligence based techniques like the whale, spider, dragonfly, and raven which are used for load balancing in the cloud. The results and analysis are presented over performance metrics such as total execution time, response time, resource utilization rate, and throughput achieved, and it is found that the performance of the raven roosting algorithm is high compared to other techniques.<\/jats:p>","DOI":"10.1515\/jisys-2019-0084","type":"journal-article","created":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T08:47:24Z","timestamp":1594370844000},"page":"40-58","source":"Crossref","is-referenced-by-count":11,"title":["Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based"],"prefix":"10.1515","volume":"30","author":[{"given":"K","family":"Bhargavi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Siddaganga Institute of Technology , Tumakuru , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B","family":"Sathish Babu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, R. V. College of Engineering , Bangalore , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeremy","family":"Pitt","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Imperial College , London England"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2020,7,3]]},"reference":[{"key":"2025120523494775410_j_jisys-2019-0084_ref_001","doi-asserted-by":"crossref","unstructured":"A. D. Josep, R. Katz, A. KonWinSKi, L. E. E. Gunho, D. Patterson and A. Rabkin, A view of cloud computing. Communications of the ACM 53(2010), 130-143.","DOI":"10.1145\/1721654.1721672"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_002","doi-asserted-by":"crossref","unstructured":"R. Z. Khan and M. O. Ahmad, Load balancing challenges in cloud computing: a survey, in: Proceedings of the International Conference on Signal, Networks, Computing, and Systems. pp. 25-32, 2016.","DOI":"10.1007\/978-81-322-3589-7_3"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_003","unstructured":"A. S. Kumar and P. Tripathi, Various issues and challenges of load balancing over cloud: a survey, International Journal Of Engineering And Computer Science 5(2016), 17517-17524."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_004","doi-asserted-by":"crossref","unstructured":"V. K. Reddy, K. D. Surya, M. S. Praveen, B. Lokesh, A. Vishal and K. Akhil, Performance analysis of Load Balancing Algorithms in cloud computing environment, Indian Journal of Science and Technology 9(2016), 1-7.","DOI":"10.17485\/ijst\/2016\/v9i18\/90697"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_005","doi-asserted-by":"crossref","unstructured":"R. Kumar and T. Prashar, Performance analysis of load balancing algorithms in cloud computing, International Journal of Computer Applications 120(2015), 19-27.","DOI":"10.5120\/21240-4016"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_006","unstructured":"M. Awan and M. A. Shah, A survey on task scheduling algorithms in cloud computing environment, International Journal of Computer and Information Technology 4(2015), 441-448."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_007","unstructured":"I.. Fister Jr, X. S. Yang, I. Fister, J. Brest and D. Fister, A brief review of nature-inspired algorithms for optimization, arXiv preprint arXiv:1307.4186 80(2013), 1-7."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_008","unstructured":"M. Dixit, N. Upadhyay and S. Silakari, An exhaustive survey on nature inspired optimization algorithms, International Journal of Software Engineering and Its Applications 9(2015), 91-104."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_009","doi-asserted-by":"crossref","unstructured":"E. Elbeltagi, T. Hegazy and D. Grierson, Comparison among five evolutionary-based optimization algorithms, Advanced engineering informatics 19(2005), 43-53.","DOI":"10.1016\/j.aei.2005.01.004"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_010","doi-asserted-by":"crossref","unstructured":"X. S. Yang, Recent advances in swarm intelligence and evolutionary computation Berlin: Springer , 585(2015).","DOI":"10.1007\/978-3-319-13826-8"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_011","doi-asserted-by":"crossref","unstructured":"T. O. Ting, X. S. Yang, S. Cheng and K.Huang, Hybrid metaheuristic algorithms: past, present, and future. In Recent advances in swarm intelligence and evolutionary computation, pp. 71-83, 2015.","DOI":"10.1007\/978-3-319-13826-8_4"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_012","doi-asserted-by":"crossref","unstructured":"A. Slowik and H. Kwasnicka, Nature inspired methods and their industry applications-Swarm intelligence algorithms, IEEE Transactions on Industrial Informatics 14(2018), 1004-1015.","DOI":"10.1109\/TII.2017.2786782"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_013","doi-asserted-by":"crossref","unstructured":"M. Kalra and S. Singh, A review of metaheuristic scheduling techniques in cloud computing. Egyptian informatics journal 16(2015), 275-295.","DOI":"10.1016\/j.eij.2015.07.001"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_014","unstructured":"H. A. Akkar and F. R. Mahdi, Evolutionary algorithms performance comparison for optimizing unimodal and multimodal test functions, International Journal of Scientific & Technology Research 4(2015), 38-45."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_015","doi-asserted-by":"crossref","unstructured":"P. P. Prajapati and M. V. Shah, Performance estimation of differential evolution, particle swarm optimization and cuckoo search algorithms, International Journal of Intelligent Systems and Applications 10(2018), 59-67.","DOI":"10.5815\/ijisa.2018.06.07"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_016","doi-asserted-by":"crossref","unstructured":"S. Mirjalili and A. Lewis, The whale optimization algorithm, Advances in engineering software 95(2016), 51-67.","DOI":"10.1016\/j.advengsoft.2016.01.008"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_017","doi-asserted-by":"crossref","unstructured":"K. Sreenu and M. Sreelatha, W-Scheduler: whale optimization for task scheduling in cloud computing, Cluster Computing (2017), 1-12.","DOI":"10.1007\/s10586-017-1055-5"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_018","doi-asserted-by":"crossref","unstructured":"I. N. Trivedi, J. Pradeep, J. Narottam, K. Arvind, K. and L. Dilip, Novel adaptive whale optimization algorithm for global optimization. Indian Journal of Science and Technology 9(2016), 319-326.","DOI":"10.17485\/ijst\/2016\/v9i38\/101939"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_019","unstructured":"H. Hu, Y. Bai and T. Xu, T, A whale optimization algorithm with inertia weight, WSEAS Trans. Comput 15(2016), 319-326."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_020","unstructured":"H. Hu, Y. Bai, Y and T. Xu, Improved whale optimization algorithms based on inertia weights and theirs applications, Int. J. Circuits, Systems Signal Process 11(2017 12-26."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_021","doi-asserted-by":"crossref","unstructured":"G. Kaur and S. Arora, Chaotic whale optimization algorithm, Journal of Computational Design and Engineering 5(2018), 275-284.","DOI":"10.1016\/j.jcde.2017.12.006"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_022","unstructured":"P. Abrol, S. Gupta, S and K. Kaur, Social spider cloud web algorithm (SSCWA): a new meta-heuristic for avoiding premature convergence in cloud, International Journal of Innovative Research in Computer and Communication Engineering 3(2015), 5698-5704."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_023","doi-asserted-by":"crossref","unstructured":"E. Cuevas and M. Cienfuegos, A new algorithm inspired in the behavior of the social-spider for constrained optimization, Expert Systems with Applications 41(2014), 412-425.","DOI":"10.1016\/j.eswa.2013.07.067"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_024","doi-asserted-by":"crossref","unstructured":"E. Cuevas, M. Cienfuegos, R. Rojas and A. Padilla, A computational intelligence optimization algorithm based on the behavior of the social-spider. In Computational Intelligence Applications in Modeling and Control pp. 123-146, 2015.","DOI":"10.1007\/978-3-319-11017-2_6"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_025","unstructured":"B. Shanmugapriya and S. Meera, S, A survey of parallel social spider optimization algorithm based on swarm intelligence for high dimensional datasets, International Journal of Computational Intelligence Research 13(2017), 2259-2265."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_026","unstructured":"C. Erredir, M. L. Riabi, E. Bouarroudj and H.Ammari, Swarm optimization algorithm inspired in the behavior of the social-spider for microwave filters optimization. In 7th African Conference on Non Destructive Testing ACNDT 2016 & the 5th International Conference on NDT and Materials Industry and Alloys (IC-WNDT-MI) pp. 6374-6384, 2016."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_027","doi-asserted-by":"crossref","unstructured":"B. Gunnarsson and K. Wiklander, K, Foraging mode of spiders affects risk of predation by birds, Biological journal of the Linnean Society 115(2015), 58-68.","DOI":"10.1111\/bij.12489"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_028","doi-asserted-by":"crossref","unstructured":"J. Q. James and V. O. Li, A social spider algorithm for global optimization, Applied Soft Computing 30(2015), 614-627.","DOI":"10.1016\/j.asoc.2015.02.014"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_029","doi-asserted-by":"crossref","unstructured":"Z. Amini, M. Maeen and M. R. Jahangir, Providing a load balancing method based on dragonfly optimization algorithm for resource allocation in cloud computing, International Journal of Networked and Distributed Computing 6(2017), 35-42.","DOI":"10.2991\/ijndc.2018.6.1.4"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_030","doi-asserted-by":"crossref","unstructured":"V. Polepally and K. S. Chatrapati, Dragonfly optimization and constraint measure-based load balancing in cloud computing, Cluster Computing pp. 1-13, 2017.","DOI":"10.1007\/s10586-017-1056-4"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_031","doi-asserted-by":"crossref","unstructured":"S. Mirjalili, Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Computing and Applications 27(2016), 1053-1073.","DOI":"10.1007\/s00521-015-1920-1"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_032","doi-asserted-by":"crossref","unstructured":"O. Bozorg-Haddad, Advanced optimization by nature-inspired algorithms, Singapore: Springer 2018.","DOI":"10.1007\/978-981-10-5221-7"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_033","unstructured":"D. Nanda and A. Chhabra, Deadline awaremulti-objective dragonfly optimization technique for scheduling jobs inmulti-cluster environment, International Journal of Applied Engineering Research 13(2018), 10286-10292."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_034","unstructured":"E. Rani and H. Kaur, Efficient Load Balancing Task Scheduling in Cloud Computing using Raven Roosting Optimization Algorithm. International Journal of Advanced Research in Computer Science 8(2017), 2419-2424."},{"key":"2025120523494775410_j_jisys-2019-0084_ref_035","doi-asserted-by":"crossref","unstructured":"A. Brabazon, W. Cui and M. O\u2019Neill, The raven roosting optimisation algorithm, Soft Computing 20(2016), 525-545.","DOI":"10.1007\/s00500-014-1520-5"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_036","doi-asserted-by":"crossref","unstructured":"S. Torabi and F. Safi-Esfahani, Improved Raven Roosting Optimization algorithm (IRRO), Swarm and Evolutionary Computation 40(2018), 144-154.","DOI":"10.1016\/j.swevo.2017.11.006"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_037","doi-asserted-by":"crossref","unstructured":"S. Torabi and F. Safi-Esfahani, A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing, The Journal of Supercomputing 74(2018), 2581-2626.","DOI":"10.1007\/s11227-018-2291-z"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_038","doi-asserted-by":"crossref","unstructured":"A. Braun and T. Bugnyar, Social bonds and rank acquisition in raven nonbreeder aggregations. Animal behaviour 84(2012), 1507-1515.","DOI":"10.1016\/j.anbehav.2012.09.024"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_039","doi-asserted-by":"crossref","unstructured":"T. Bugnyar, Social cognition in ravens. Comparative cognition & behavior reviews 8(2013), 1.","DOI":"10.3819\/ccbr.2013.80001"},{"key":"2025120523494775410_j_jisys-2019-0084_ref_040","unstructured":"http:\/\/www.cloudbus.org\/cloudsim\/"}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/view\/journals\/jisys\/30\/1\/article-p40.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2019-0084\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2019-0084\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:50:56Z","timestamp":1764978656000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2019-0084\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,3]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,8,15]]},"published-print":{"date-parts":[[2020,8,15]]}},"alternative-id":["10.1515\/jisys-2019-0084"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2019-0084","relation":{},"ISSN":["2191-026X"],"issn-type":[{"value":"2191-026X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,3]]}}}