{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:58:09Z","timestamp":1775066289999,"version":"3.50.1"},"reference-count":30,"publisher":"Wiley","issue":"13","license":[{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Communication"],"published-print":{"date-parts":[[2021,9,10]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Cloud computing is the current computing standard, which provides information technology (IT) services over the Internet on demand. In the cloud environment, a task is mapped with an available resource to attain a good result. Task scheduling is the technique that is used to allocate tasks on virtual machines (VMs) of a server based on its capacity of workload. Tasks are scheduled to the server in such a way to minimize traffic and time delay. Particle swarm optimization (PSO) is the best existing algorithm used to schedule a task to an existing resource on the environment of the cloud. By PSO, the task is scheduled for an existing resource to reduce computational cost. In this paper, a hybrid swarm optimization (HSO) algorithm, which is the combination of PSO and salp swarm optimization (SSO), is proposed to resolve task scheduling issues in the cloud environment. The main goal of HSO is to schedule the task to the available resource in such a way to reduce the execution time and computation cost. Multilayer logistic regression (MLR) is an approach used to detect the overloaded VMs, so that a task can be scheduled to a VM according to its capacity of workload. The proposed HSO algorithm with MLR is simulated on the cloudsim toolkit, and the results reveal the efficiency of the proposed algorithm in terms of cost, execution time, and makespan. Compared to the existing algorithms such as the genetic algorithms (GAs), the improved efficiency evolution (IDEA), and the PSO, the proposed algorithm reveals superiority in terms of efficiency, resource utilization, and speed.<\/jats:p>","DOI":"10.1002\/dac.4694","type":"journal-article","created":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T02:16:24Z","timestamp":1624932984000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Hybrid swarm optimization algorithm based on task scheduling in a cloud environment"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1058-7656","authenticated-orcid":false,"given":"Heba M.","family":"Eldesokey","sequence":"first","affiliation":[{"name":"Logicom Distribution Company  Kuwait"},{"name":"Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering Menoufia University  Menouf Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0979-4292","authenticated-orcid":false,"given":"Saied M.","family":"Abd El\u2010atty","sequence":"additional","affiliation":[{"name":"Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering Menoufia University  Menouf Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7509-2120","authenticated-orcid":false,"given":"Walid","family":"El\u2010Shafai","sequence":"additional","affiliation":[{"name":"Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering Menoufia University  Menouf Egypt"},{"name":"Security Engineering Lab Computer Science Department, Prince Sultan University  Riyadh Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1704-7211","authenticated-orcid":false,"given":"Mohammed","family":"Amoon","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Community College King Saud University  Riyadh Saudi Arabia"},{"name":"Department of Computer Science and Engineering, Faculty of Electronic Engineering Menoufia University  Menouf Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4117-3496","authenticated-orcid":false,"given":"Fathi E.","family":"Abd El\u2010Samie","sequence":"additional","affiliation":[{"name":"Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering Menoufia University  Menouf Egypt"},{"name":"Department of Information Technology College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University  Riyadh Saudi Arabia"}]}],"member":"311","published-online":{"date-parts":[[2021,6,28]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4123"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13673-019-0174-9"},{"key":"e_1_2_7_4_1","first-page":"10","article-title":"Energy\u2010aware VM initial placement strategy based on BPSO in cloud computing","volume":"2018","author":"Fu X","year":"2018","journal-title":"Sci Prog"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.05.056"},{"key":"e_1_2_7_6_1","first-page":"1943","article-title":"Optimal cloud computing resource allocation for demand\u2010side management in smart grid","volume":"8","author":"Cao Z","year":"2016","journal-title":"IEEE Trans Smart Grid"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-015-1074-0"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-018-1823-x"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2019.12.028"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2019.102038"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.suscom.2020.100373"},{"key":"e_1_2_7_12_1","doi-asserted-by":"crossref","unstructured":"KumarAS ParthibanK ShankarSS.An efficient task scheduling in a cloud computing environment using hybrid genetic algorithm\u2010particle swarm optimization (GA\u2010PSO) algorithm. In 2019 International Conference on Intelligent Sustainable Systems (ICISS) 2019:29\u201034.","DOI":"10.1109\/ISS1.2019.8908041"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1097-7_81"},{"key":"e_1_2_7_14_1","doi-asserted-by":"crossref","unstructured":"ThanhKB XuanLMH KhacCN DacHH TranVP CongHT.An auto\u2010scaling VM game approach for multi\u2010tier application with particle swarm optimization algorithm in cloud computing. In 2018International Conference on Advanced Technologies for Communications (ATC) 2018:326\u2010331.","DOI":"10.1109\/ATC.2018.8587526"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2016.01.003"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2818680"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2015.08.006"},{"key":"e_1_2_7_18_1","doi-asserted-by":"crossref","unstructured":"AgarwalM SrivastavaGMS.A genetic algorithm inspired task scheduling in cloud computing. In2016 International Conference on Computing Communication and Automation (ICCCA) 2016:364\u2010367.","DOI":"10.1109\/CCAA.2016.7813746"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-1071-1"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-017-2905-z"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.09.014"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2016.2575779"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-017-1534-8"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-0589-4_49"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-019-02915-3"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-0773-8"},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105686"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2017.07.002"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tree.2016.06.007"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.12.022"},{"key":"e_1_2_7_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eij.2016.12.002"}],"container-title":["International Journal of Communication Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/dac.4694","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/dac.4694","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/dac.4694","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T07:21:07Z","timestamp":1693293667000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/dac.4694"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,28]]},"references-count":30,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2021,9,10]]}},"alternative-id":["10.1002\/dac.4694"],"URL":"https:\/\/doi.org\/10.1002\/dac.4694","archive":["Portico"],"relation":{},"ISSN":["1074-5351","1099-1131"],"issn-type":[{"value":"1074-5351","type":"print"},{"value":"1099-1131","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,28]]},"assertion":[{"value":"2020-06-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-10-27","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e4694"}}