{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:44:02Z","timestamp":1772822642765,"version":"3.50.1"},"reference-count":43,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["SIMULATION"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p>The growing demand for cloud computing services has led to a rapid expansion of cloud data centers (CDCs), significantly increasing global energy consumption, driven by underutilized physical machines and continuous cooling overhead. To address these challenges, cloud providers are adopting green solutions such as dynamic consolidation, which minimizes the number of active physical machines while maintaining system performance. In this comparative study, we model task arrivals using five probability distributions (Normal, L\u00e9vy, Pareto, Chi-square, and Binomial) to explore their impact on the scheduling efficiency of six well-established metaheuristic algorithms; genetic algorithm (GA), ant colony optimization (ACO), cuckoo search (CS), particle swarm optimization (PSO), artificial bee colony (ABC), and simulated annealing (SA). By introducing probabilistic variation in task arrival times, the study examines the sensitivity of these algorithms to dynamic workloads. Using the CloudSim simulator, performance is assessed across small- and large-scale CDC environments based on makespan, energy consumption, and resource utilization. Results reveal that Pareto-distributed task arrivals yield consistently strong performance in small-scale scenarios, while PSO paired with chi-square distributions outperforms others in large-scale settings.<\/jats:p>","DOI":"10.1177\/00375497251356490","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T10:06:20Z","timestamp":1753869980000},"page":"1133-1151","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Optimizing workflow scheduling for efficient resource utilization in scalable cloud computing data centers"],"prefix":"10.1177","volume":"101","author":[{"given":"Hind","family":"Mikram","sequence":"first","affiliation":[{"name":"Computer, Networks, Modeling, and Mobility Laboratory (IR2M), Faculty of Sciences and Techniques, Hassan First University of Settat, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9282-5154","authenticated-orcid":false,"given":"Said","family":"El Kafhali","sequence":"additional","affiliation":[{"name":"Computer, Networks, Modeling, and Mobility Laboratory (IR2M), Faculty of Sciences and Techniques, Hassan First University of Settat, Morocco"}]}],"member":"179","published-online":{"date-parts":[[2025,7,30]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09573-y"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-023-01182-w"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.120745"},{"key":"e_1_3_3_5_2","first-page":"1","article-title":"Server consolidation algorithms for cloud computing: taxonomies and systematic analysis of literature","volume":"12","author":"Mikram H","year":"2022","unstructured":"Mikram H, El Kafhali S, Saadi Y. Server consolidation algorithms for cloud computing: taxonomies and systematic analysis of literature. Int J Cloud Appl Comput 2022; 12: 1\u201324.","journal-title":"Int J Cloud Appl Comput"},{"key":"e_1_3_3_6_2","first-page":"100686","article-title":"Energy aware resource optimization using unified meta-heuristic optimization algorithm allocation for cloud computing environment","volume":"35","author":"Al-Wesabi FN","year":"2022","unstructured":"Al-Wesabi FN, Obayya M, Hamza MA, et al. Energy aware resource optimization using unified meta-heuristic optimization algorithm allocation for cloud computing environment. Sustain Comput Inform Syst 2022; 35: 100686.","journal-title":"Sustain Comput Inform Syst"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10723-023-09696-5"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-018-3196-0"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-04839-2"},{"key":"e_1_3_3_10_2","first-page":"100605","article-title":"A novel multi-objective CR-PSO task scheduling algorithm with deadline constraint in cloud computing","volume":"32","author":"Dubey K","year":"2021","unstructured":"Dubey K, Sharma SC. A novel multi-objective CR-PSO task scheduling algorithm with deadline constraint in cloud computing. Sustain Comput Inform Syst 2021; 32: 100605.","journal-title":"Sustain Comput Inform Syst"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-22458-9"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-18993-0"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/biomimetics7040204"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-020-04847-2"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10922-020-09577-2"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3048438"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2023.102864"},{"key":"e_1_3_3_18_2","first-page":"1","volume-title":"2023 IEEE 6th international conference on cloud computing and artificial intelligence: technologies and applications (CloudTech)","author":"Mikram H","unstructured":"Mikram H, El Kafhali S, Saadi Y. A hybrid algorithm based on PSO algorithm and chi-squared distribution for tasks consolidation in cloud computing environment. In: 2023 IEEE 6th international conference on cloud computing and artificial intelligence: technologies and applications (CloudTech), Marrakech, Morocco, 21\u201323 November 2023, pp. 1\u20136. New York: IEEE."},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2021.06.079"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1177\/15501477211023021"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-03807-3"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5575129"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.3390\/app12042160"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.09.039"},{"key":"e_1_3_3_25_2","first-page":"100995","article-title":"A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers","volume":"26","author":"Sayadnavard MH","year":"2022","unstructured":"Sayadnavard MH, Haghighat AT, Rahmani AM. A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers. Eng Sci Technol Int J 2022; 26: 100995.","journal-title":"Eng Sci Technol Int J"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-7386-1_39"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2006.329691"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-7401-9"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.11.014"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3927.001.0001"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcm.2008.10.013"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.108487"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/0-306-48056-5_10"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csi.2017.07.001"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100645"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116834"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2020.113301"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.06.011"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.icheatmasstransfer.2022.106124"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2022.08.019"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2015.12.004"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-27762-7_44"},{"key":"e_1_3_3_43_2","first-page":"1","volume-title":"2009 international conference on high performance computing & simulation","author":"Buyya R","unstructured":"Buyya R, Ranjan R, Calheiros RN. Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: challenges and opportunities. In: 2009 international conference on high performance computing & simulation, Leipzig, Germany, 21\u201324 June 2009, pp. 1\u201311. New York: IEEE."},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2019.04.030"}],"container-title":["SIMULATION"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00375497251356490","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/00375497251356490","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00375497251356490","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T07:00:26Z","timestamp":1761980426000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/00375497251356490"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,30]]},"references-count":43,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1177\/00375497251356490"],"URL":"https:\/\/doi.org\/10.1177\/00375497251356490","relation":{},"ISSN":["0037-5497","1741-3133"],"issn-type":[{"value":"0037-5497","type":"print"},{"value":"1741-3133","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,30]]}}}