{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T23:27:12Z","timestamp":1774654032239,"version":"3.50.1"},"reference-count":35,"publisher":"IGI Global","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<p>Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.<\/p>","DOI":"10.4018\/ijamc.2019100101","type":"journal-article","created":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T12:14:21Z","timestamp":1565180061000},"page":"1-17","source":"Crossref","is-referenced-by-count":24,"title":["A PSO Algorithm Based Task Scheduling in Cloud Computing"],"prefix":"10.4018","volume":"10","author":[{"given":"Mohit","family":"Agarwal","sequence":"first","affiliation":[{"name":"Dayalbagh Educational Institute, Agra, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gur Mauj Saran","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Dayalbagh Educational Institute, Agra, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2432","reference":[{"key":"IJAMC.2019100101-0","doi-asserted-by":"publisher","DOI":"10.5815\/ijmecs.2017.12.05"},{"key":"IJAMC.2019100101-1","doi-asserted-by":"publisher","DOI":"10.1109\/CCAA.2016.7813746"},{"key":"IJAMC.2019100101-2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-3773-3_29"},{"key":"IJAMC.2019100101-3","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-014-0398-5"},{"key":"IJAMC.2019100101-4","doi-asserted-by":"publisher","DOI":"10.1145\/65979.65981"},{"key":"IJAMC.2019100101-5","doi-asserted-by":"publisher","DOI":"10.1002\/spe.995"},{"key":"IJAMC.2019100101-6","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2012.113012.120310"},{"key":"IJAMC.2019100101-7","unstructured":"Guo, L. Z. (2013). Particle swarm optimization embedded in variable neighbourhood search for task scheduling in cloud computing. J. Donghua Univ. (Eng. Ed.), 30(2), 145\u2013152."},{"key":"IJAMC.2019100101-8","doi-asserted-by":"publisher","DOI":"10.1137\/1024022"},{"key":"IJAMC.2019100101-9","doi-asserted-by":"publisher","DOI":"10.1109\/CSC.2011.6138524"},{"key":"IJAMC.2019100101-10","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"IJAMC.2019100101-11","doi-asserted-by":"publisher","DOI":"10.1109\/ChinaGrid.2011.17"},{"key":"IJAMC.2019100101-12","doi-asserted-by":"publisher","DOI":"10.1109\/cloud.2011.110"},{"key":"IJAMC.2019100101-13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-30976-2_17"},{"key":"IJAMC.2019100101-14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2013.06.004"},{"key":"IJAMC.2019100101-15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25789-6_32"},{"key":"IJAMC.2019100101-16","doi-asserted-by":"publisher","DOI":"10.1016\/j.protcy.2012.05.128"},{"key":"IJAMC.2019100101-17","doi-asserted-by":"publisher","DOI":"10.1109\/ccgrid.2009.93"},{"key":"IJAMC.2019100101-18","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.07.385"},{"key":"IJAMC.2019100101-19","doi-asserted-by":"publisher","DOI":"10.1007\/s10878-013-9644-6"},{"key":"IJAMC.2019100101-20","doi-asserted-by":"publisher","DOI":"10.1109\/MPOT.2013.2279684"},{"key":"IJAMC.2019100101-21","doi-asserted-by":"publisher","DOI":"10.1016\/S0141-9331(02)00053-4"},{"key":"IJAMC.2019100101-22","doi-asserted-by":"publisher","DOI":"10.1007\/s13198-016-0495-2"},{"key":"IJAMC.2019100101-23","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-016-2166-2"},{"key":"IJAMC.2019100101-24","doi-asserted-by":"crossref","unstructured":"Shengjun, X., Jie, Z., & Xiaolong, X. (2012). An improved algorithm based on ACO for cloud service PDTs scheduling. Advances in Information Sciences and Service Sciences, 4(18).","DOI":"10.4156\/aiss.vol4.issue18.41"},{"key":"IJAMC.2019100101-25","first-page":"591","article-title":"Parameter selection in particle swarm optimization.","author":"Y.Shi","year":"1998","journal-title":"International conference on evolutionary programming"},{"key":"IJAMC.2019100101-26","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.1999.785511"},{"key":"IJAMC.2019100101-27","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.01.036"},{"key":"IJAMC.2019100101-28","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2014.2315797"},{"key":"IJAMC.2019100101-29","unstructured":"Wang, J., Li, F., & Zhang, L. Q. (2014). Apply PSO into cloud storage task scheduling with QoS preference awareness. Journal on Communications, 3, 027."},{"key":"IJAMC.2019100101-30","doi-asserted-by":"crossref","unstructured":"Wu, L., Wang, Y. J., & Yan, C. K. (2014). Performance Comparison of Energy-Aware Task Scheduling with GA and CRO Algorithms in Cloud Environment. Applied Mechanics and Materials, 596, 204\u2013208. doi:10.4028\/www.scientific.net\/amm.596.204","DOI":"10.4028\/www.scientific.net\/AMM.596.204"},{"key":"IJAMC.2019100101-31","doi-asserted-by":"publisher","DOI":"10.4304\/jsw.9.2.466-473"},{"key":"IJAMC.2019100101-32","doi-asserted-by":"publisher","DOI":"10.14257\/ijunesst.2016.9.1.36"},{"key":"IJAMC.2019100101-33","doi-asserted-by":"publisher","DOI":"10.1109\/71.954620"},{"key":"IJAMC.2019100101-34","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2015.2508940"}],"container-title":["International Journal of Applied Metaheuristic Computing"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=234684","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T15:06:44Z","timestamp":1684422404000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJAMC.2019100101"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2019,10,1]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,10]]}},"URL":"https:\/\/doi.org\/10.4018\/ijamc.2019100101","relation":{},"ISSN":["1947-8283","1947-8291"],"issn-type":[{"value":"1947-8283","type":"print"},{"value":"1947-8291","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,1]]}}}