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Cloud service providers have different types of virtual machines with different computing power types in multi\u2010layered architectures. Various optimization works have been proposed to tackle the load balancing problem in cloud service providers. Improving performance in load balancing is a cumbersome task. Seven stone game optimization (SSGO) is designed based on the south Indian seven stone game workflow. The proposed method's foremost ambition is to reduce makespan time and maximize cloud service providers' utilization. The proposed method was simulated, and results demonstrate that minimizes the makespan time and maximizes the resource utilization than the particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), and Tabu search (TS). The experimental results show that the SSGO provides 4% more resource utilization than PSO, 5% more than GA, and 7% more than SA and 10% more than TS.<\/jats:p>","DOI":"10.1002\/spe.2954","type":"journal-article","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T07:16:29Z","timestamp":1610003789000},"page":"1242-1258","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/http\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An efficient load balancing using seven stone game optimization in cloud computing"],"prefix":"10.1002","volume":"51","author":[{"given":"Periyasami","family":"Karthikeyan","sequence":"first","affiliation":[{"name":"Faculty of Engineering &amp; Technology, Parul Institute of Technology Parul University  Vadodara India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,1,6]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.09.021"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2016.11.003"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2016.01.018"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2017.11.016"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.08.006"},{"key":"e_1_2_7_7_1","first-page":"1","article-title":"Hybridization of a meta\u2010heuristic algorithm for load balancing in cloud computing environment","author":"Jena UK","year":"2020","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.12.004"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2600"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2584"},{"key":"e_1_2_7_11_1","article-title":"Load balancing optimization based on hybrid heuristic\u2010metaheuristic techniques in cloud environment","author":"Kaur A","year":"2019","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2015.06.001"},{"key":"e_1_2_7_13_1","first-page":"237","article-title":"LNCS 8274 \u2010 task scheduling optimization in cloud computing applying multi\u2010objective particle swarm optimization","volume":"8274","author":"Ramezani F","year":"2013","journal-title":"LNCS"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2013.01.025"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11047-009-9176-2"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2015.04.017"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eij.2017.07.001"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-016-3481-8"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2018.12.010"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2803"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2016.2572161"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-014-1124-y"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2014.2342378"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2014.2385698"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2016.07.007"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2019.106860"},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2019.05.022"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2015.06.025"},{"key":"e_1_2_7_29_1","doi-asserted-by":"crossref","unstructured":"CalheirosRN BuyyaR.Energy\u2010efficient scheduling of urgent bag\u2010of\u2010tasks applications in clouds through DVFS. 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