{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T09:26:57Z","timestamp":1770715617683,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Priority in task scheduling and resource allocation for cloud computing has attracted significant attention from the research community. However, traditional scheduling algorithms often lack the ability to differentiate between tasks with varying levels of importance. This limitation presents a challenge when cloud servers must handle diverse tasks with distinct priority classes and strict quality of service requirements. To address these challenges in cloud computing environments, particularly within the infrastructure of service models, we propose a novel, self-adaptive, multiclass priority algorithm with VM clustering for resource allocation. This algorithm implements a four-tiered prioritization system to optimize key objectives, including makespan and energy consumption, while simultaneously optimizing resource utilization, degree of imbalance, and waiting time. Additionally, we propose a resource prioritization and load-balancing model based on the clustering technique. The proposed work was validated through multiple simulations using the CloudSim simulator, comparing its performance against well-known task scheduling algorithms. The simulation results and analysis demonstrate that the proposed algorithm effectively optimizes makespan and energy consumption. Specifically, our work achieved percentage improvements ranging from +0.97% to +26.80% in makespan and +3.68% to +49.49% in energy consumption while also improving other performance metrics, including throughput, resource utilization, and load balancing. This novel model demonstrably enhances task scheduling and resource allocation efficiency, particularly in complex scenarios with tight deadlines and multiclass priorities.<\/jats:p>","DOI":"10.3390\/computers14030081","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T10:55:48Z","timestamp":1740480948000},"page":"81","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel, Self-Adaptive, Multiclass Priority Algorithm with VM Clustering for Efficient Cloud Resource Allocation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9632-9246","authenticated-orcid":false,"given":"Hicham","family":"Ben Alla","sequence":"first","affiliation":[{"name":"LAVETE Laboratory, Mathematics and Computer Science Department, Science and Technical Faculty, Hassan 1 University, Settat 26000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1089-9948","authenticated-orcid":false,"given":"Said","family":"Ben Alla","sequence":"additional","affiliation":[{"name":"LAVETE Laboratory, Mathematics and Computer Science Department, Science and Technical Faculty, Hassan 1 University, Settat 26000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdellah","family":"Ezzati","sequence":"additional","affiliation":[{"name":"LAVETE Laboratory, Mathematics and Computer Science Department, Science and Technical Faculty, Hassan 1 University, Settat 26000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8891-180X","authenticated-orcid":false,"given":"Abdellah","family":"Touhafi","sequence":"additional","affiliation":[{"name":"Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/978-3-319-28034-9_8","article-title":"An Efficient Task Consolidation Algorithm for Cloud Computing Systems","volume":"61","author":"Panda","year":"2016","journal-title":"Distrib. Comput. Internet Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.protcy.2013.12.525","article-title":"Advantages and Challenges of Adopting Cloud Computing from an Enterprise Perspective","volume":"12","author":"Avram","year":"2014","journal-title":"Procedia Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"814","DOI":"10.17485\/ijst\/2015\/v8i9\/50180","article-title":"An Analysis on Efficient Resource Allocation Mechanisms in Cloud Computing","volume":"8","author":"Shyamala","year":"2015","journal-title":"Indian J. Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1007\/s10586-022-03713-0","article-title":"Energy Efficiency in Cloud Computing Data Centers: A Survey on Software Technologies","volume":"26","author":"Katal","year":"2023","journal-title":"Clust. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11514","DOI":"10.1007\/s11227-021-03741-4","article-title":"A Novel Multiclass Priority Algorithm for Task Scheduling in Cloud Computing","volume":"77","author":"Ezzati","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e5186","DOI":"10.1002\/cpe.5186","article-title":"Applying Queue Theory for Modeling of Cloud Computing: A Systematic Review","volume":"31","author":"Rahmani","year":"2019","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1502242","DOI":"10.1080\/23311916.2018.1502242","article-title":"A Review of Multi-Objective Optimization: Methods and Its Applications","volume":"5","author":"Gunantara","year":"2018","journal-title":"Cogent Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"80716","DOI":"10.1109\/ACCESS.2020.2988796","article-title":"Unsupervised K-Means Clustering Algorithm","volume":"8","author":"Sinaga","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107789","DOI":"10.1016\/j.compeleceng.2022.107789","article-title":"QoS Aware Task Consolidation Approach for Maintaining SLA Violations in Cloud Computing","volume":"99","author":"Singh","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Liu, L., Pu, C., Dou, Q., Wu, L., and Zhou, W. (2018). A Comparative Study of Containers and Virtual Machines in Big Data Environment. arXiv.","DOI":"10.1109\/CLOUD.2018.00030"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s11633-012-0664-y","article-title":"Task-Resource Scheduling Problem","volume":"9","author":"Gorbenko","year":"2012","journal-title":"Int. J. Autom. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13677-018-0105-8","article-title":"Task Scheduling and Resource Allocation in Cloud Computing Using a Heuristic Approach","volume":"7","author":"Gawali","year":"2018","journal-title":"J. Cloud Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ben Alla, H., Ben Alla, S., Ezzati, A., and Touhafi, A. (2016, January 21\u201323). An Efficient Dynamic Priority-Queue Algorithm Based on AHP and PSO for Task Scheduling in Cloud Computing. Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016), Marrakech, Morocco.","DOI":"10.1007\/978-3-319-52941-7_14"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mangalampalli, S., Swain, S.K., Chakrabarti, T., Chakrabarti, P., Karri, G.R., Margala, M., Unhelkar, B., and Krishnan, S.B. (2023). Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization. Sensors, 23.","DOI":"10.3390\/s23136155"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4475","DOI":"10.1007\/s40747-021-00479-7","article-title":"EHEFT-R: Multi-Objective Task Scheduling Scheme in Cloud Computing","volume":"8","author":"Zhang","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.1007\/s10586-018-2515-2","article-title":"Task Scheduling in a Cloud Computing Environment Using HGPSO Algorithm","volume":"22","author":"Venkatesan","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"27111","DOI":"10.1109\/ACCESS.2023.3255781","article-title":"Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach","volume":"11","author":"Lipsa","year":"2023","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.future.2023.09.005","article-title":"SG-PBFS: Shortest Gap-Priority Based Fair Scheduling Technique for Job Scheduling in Cloud Environment","volume":"150","author":"Murad","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1650119","DOI":"10.1142\/S021812661650119X","article-title":"Priority-Based Task Scheduling in the Cloud Systems Using a Memetic Algorithm","volume":"25","author":"Keshanchi","year":"2016","journal-title":"J. Circuits Syst. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1007\/s10586-021-03280-w","article-title":"PLB: A Resilient and Adaptive Task Scheduling Scheme Based on Multi-Queues for Cloud Environment","volume":"24","author":"Sharma","year":"2021","journal-title":"Clust. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e7376","DOI":"10.1002\/cpe.7376","article-title":"A Multi-Queue Priority-Based Task Scheduling Algorithm in Fog Computing Environment","volume":"34","author":"Fahad","year":"2022","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yu, Y., and Su, Y. (2019, January 12\u201314). Cloud Task Scheduling Algorithm Based on Three Queues and Dynamic Priority. Proceedings of the 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China.","DOI":"10.1109\/ICPICS47731.2019.8942588"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e4126","DOI":"10.1002\/dac.4126","article-title":"Multicriteria Decision-Making Techniques for Avoiding Similar Task Scheduling Conflict in Cloud Computing","volume":"33","author":"Nayak","year":"2019","journal-title":"Int. J. Commun. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1016\/j.future.2019.11.019","article-title":"BigTrustScheduling: Trust-Aware Big Data Task Scheduling Approach in Cloud Computing Environments","volume":"110","author":"Rjoub","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4429","DOI":"10.1002\/cpe.3767","article-title":"Dynamic Resource Demand Prediction and Allocation in Multi-Tenant Service Clouds","volume":"28","author":"Verma","year":"2016","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1016\/j.future.2011.04.017","article-title":"Energy-Aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing","volume":"28","author":"Beloglazov","year":"2012","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1007\/s10586-018-2811-x","article-title":"A Novel Task Scheduling Approach Based on Dynamic Queues and Hybrid Meta-Heuristic Algorithms for Cloud Computing Environment","volume":"21","author":"Touhafi","year":"2018","journal-title":"Clust. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ben Alla, S., Ben Alla, H., Touhafi, A., and Ezzati, A. (2018). An Efficient Energy-Aware Task Scheduling with Deadline-Constrained in Cloud Computing. Computers, 8.","DOI":"10.3390\/computers8020046"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","article-title":"The Global K-Means Clustering Algorithm","volume":"36","author":"Likas","year":"2003","journal-title":"Pattern Recognit."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1002\/spe.995","article-title":"CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms","volume":"41","author":"Calheiros","year":"2011","journal-title":"Softw. Pract. Exp."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1109\/TEVC.2008.925798","article-title":"Multiobjective Optimization Problems with Complicated Pareto Sets, MOEA\/D and NSGA-II","volume":"13","author":"Li","year":"2009","journal-title":"IEEE Trans. Evol. Computation"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1016\/j.camwa.2008.10.009","article-title":"A Novel Strategy of Pareto-Optimal Solution Searching in Multi-Objective Particle Swarm Optimization (MOPSO)","volume":"57","author":"Yang","year":"2009","journal-title":"Comput. Math. Appl."},{"key":"ref_34","unstructured":"Zitzler, E., Laumanns, M., and Thiele, L. (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm, ETH Z\u00fcrich. TIK Report."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.1016\/j.jpdc.2014.06.013","article-title":"Experience with Using the Parallel Workloads Archive","volume":"74","author":"Feitelson","year":"2014","journal-title":"J. Parallel Distrib. Comput."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/3\/81\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:41:32Z","timestamp":1760028092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/3\/81"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,24]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["computers14030081"],"URL":"https:\/\/doi.org\/10.3390\/computers14030081","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,24]]}}}