{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:01:33Z","timestamp":1790748093062,"version":"4.1.0"},"update-to":[{"DOI":"10.12688\/f1000research.177203.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000}}],"reference-count":10,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns3:p>\n                    Background The high variability of workloads makes it very difficult for cloud datacenters to efficiently schedule their tasks and resource allocation. The correct forecasting of future resource utilization allows us to proactively scale and implement more sophisticated scheduling policies, which eventually results in has potential to improve resource utilization and fewer failures. Methods This study uses the Google Cluster Trace v3 dataset, which contains a wealth of job and task data (start and end times, CPU and memory usage, scheduling class, and priority) to create supervised machine and deep-learning models that can predict future of CPU usage. Conclusions This study evaluates Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), and Neural Network (NN) models for predicting future CPU usage, which are quite successful in predicting computer resource usage (94% validation accuracy and R\n                    <ns3:sup>2<\/ns3:sup>\n                    of 0.90). These results underscore the potential of data driven approach definitely machine and deep learning-based CPU demand predictions as valuable tools for cloud schedulers, improving resource management, and reducing operational costs. Also discuss the feasibility of deploying the proposed solution on distributed platforms such as Spark and Google Cloud and outline future research directions to integrate predictive models with real-time cloud resource management.\n                  <\/ns3:p>","DOI":"10.12688\/f1000research.177203.2","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T07:29:11Z","timestamp":1782372551000},"page":"366","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent Cloud Resource Usage Potentially to Improve Task Scheduling by the use of Artificial Intelligence"],"prefix":"10.12688","volume":"15","author":[{"given":"Huda","family":"Mohammed Lateef","sequence":"first","affiliation":[{"name":"Al-Fallujah Al Anbar Governorate University of Fallujah"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6717-7364","authenticated-orcid":false,"given":"Mohammed","family":"Abduljawad Al-Shibly","sequence":"additional","affiliation":[{"name":"Al-Fallujah Al Anbar Governorate University of Fallujah"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3878-1271","authenticated-orcid":false,"given":"Ahmed","family":"Hadi Ali AL-Jumaili","sequence":"additional","affiliation":[{"name":"Al-Fallujah Al Anbar Governorate University of Fallujah"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5392-5291","authenticated-orcid":false,"given":"Omar","family":"D. Madeeh","sequence":"additional","affiliation":[{"name":"Al-Fallujah Al Anbar Governorate University of Fallujah"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7905-0978","authenticated-orcid":false,"given":"Mohammed","family":"A.S. Al-Hitawi","sequence":"additional","affiliation":[{"name":"Al-Fallujah Al Anbar Governorate University of Fallujah"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deshinta","family":"Arrova Dewi","sequence":"additional","affiliation":[{"name":"Nilai Negeri Sembilan INTI International University & Colleges"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohd","family":"Zakree bin Ahmad Nazri","sequence":"additional","affiliation":[{"name":"Faculty of Data Science and Information Technology Bangi Selangor Universiti Kebangsaan Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaima","family":"Nasir Kadhim","sequence":"additional","affiliation":[{"name":"Baqubah Diyala Governorate University of Diyala"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.18674733","article-title":"GCD-CloudAI: Benchmarking intelligent scheduling and workload prediction (Version v0) [Software].","author":"M Al-Hitawi","year":"2025","journal-title":"Zenodo."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"181","DOI":"10.12688\/f1000research.176408.1","article-title":"Enhancing Transformer-Based Language Models for Hungarian Handwritten Text Recognition.","volume":"15","author":"M Al-Hitawi","year":"2026","journal-title":"F1000Res."},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-33498-2","article-title":"A fusion deep Q-learning and particle swarm optimization algorithm for adaptive resource allocation in cloud computing circumstances.","author":"A Al-Jumaili","year":"2026","journal-title":"Scientific Report."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"50-58","DOI":"10.1145\/1721654.1721672","article-title":"A view of cloud computing.","volume":"53","author":"M Armbrust","year":"2010","journal-title":"Commun. ACM."},{"key":"ref14","doi-asserted-by":"crossref","first-page":"20240441","DOI":"10.1515\/jisys-2024-0441","article-title":"Resource allocation strategies and task scheduling algorithms for cloud computing: A systematic literature review.","volume":"34","author":"W Kareem Awad","year":"2025","journal-title":"Journal of Intelligent Systems"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"383","DOI":"10.3390\/sym17030383","article-title":"Symmetry-aware multi-dimensional attention spiking neural network with optimization techniques for accurate workload and resource time series prediction in cloud computing systems.","volume":"17","author":"T Karpagam","year":"2025","journal-title":"Symmetry."},{"key":"ref7","first-page":"2901-2915","article-title":"Deep reinforcement learning for job scheduling in cloud computing.","volume":"24","author":"X Li","year":"2021","journal-title":"Clust. Comput."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"34-41","DOI":"10.1145\/1773394.1773400","article-title":"Towards characterizing cloud backend workloads: insights from Google compute clusters.","volume":"37","author":"A Mishra","year":"2010","journal-title":"ACM SIGMETRICS Performance Evaluation Review."},{"key":"ref15","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s10723-025-09812-7","article-title":"Adaptive Resource Scheduling in Multi-Cloud Computing Using Recurrent Neural Forecasting and Memory-Based Metaheuristic Optimization.","volume":"23","author":"S Sefati","year":"2025","journal-title":"Journal of Grid Computing."},{"key":"ref16","first-page":"680-687","article-title":"Multi-agent reinforcement learning for adaptive resource orchestration in cloud-native clusters.","author":"G Yao","year":"2025","journal-title":"Proceedings of the 2nd International Conference on Intelligent Computing and Data Analysis."}],"updated-by":[{"DOI":"10.12688\/f1000research.177203.3","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000}}],"container-title":["F1000Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/f1000research.com\/articles\/15-366\/v2\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/15-366\/v2\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/15-366\/v2\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:29:10Z","timestamp":1790746150000},"score":1,"resource":{"primary":{"URL":"https:\/\/f1000research.com\/articles\/15-366\/v2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,25]]},"references-count":10,"URL":"https:\/\/doi.org\/10.12688\/f1000research.177203.2","relation":{"has-version":[{"id-type":"doi","id":"10.12688\/f1000research.177203.1","asserted-by":"subject"}],"has-review":[{"id-type":"doi","id":"10.5256\/f1000research.203354.r496558","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.203354.r496558","asserted-by":"object"}],"is-version-of":[{"id-type":"doi","id":"10.12688\/f1000research.177203.3","asserted-by":"object"}]},"ISSN":["2046-1402"],"issn-type":[{"value":"2046-1402","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,25]]},"assertion":[{"value":"Not Approved, Approved","URL":"https:\/\/f1000research.com\/articles\/15-366\/v2#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.5256\/f1000research.203354.r496558, Alexandra Theodoropoulou, University of Patras, Patras, 29 Jun 2026, version 2, 1 approved, 1 not approved","URL":"https:\/\/f1000research.com\/articles\/15-366\/v2#referee-response-496558","order":0,"name":"referee-response-496558","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Mohammed Abduljawad Al-Shibly<\/b>; \n<i>Posted: 11 Aug 2026<\/i>; We sincerely thank the reviewer for the careful assessment and constructive comments. The manuscript has been further revised to address the remaining concerns. The detailed revisions are incorporated in the updated manuscript attached to the email submitted to the Editorial Board, as summarized below:  1. Target variable definition The target variable has been clearly redefined as future CPU utilization, with prediction inputs restricted to information available at or before task dispatch. Post-execution variables were excluded to avoid information leakage.  2. Consistency of models and results The model set, figures, tables, and terminology have been revised for consistency. Logistic Regression and Decision Tree have been removed, and the evaluated models are now consistently presented as Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), Neural Network (NN), and Cluster-Based Ensemble (CBE).  3. Neural-network classification optimization The unsupported claim regarding optimization for classification accuracy has been removed. The NN is now presented primarily as a regression model, while high-\/low-CPU classification is treated as a secondary analysis based on continuous CPU-utilization predictions.  4. Practical implications and conclusion The conclusion has been revised to avoid overstating the practical implications. The findings are now presented as demonstrating the predictive potential of the proposed models rather than proven improvements in scheduling efficiency, cost reduction, overload reduction, or resource allocation. Scheduler-level evaluation is identified as future work.  We appreciate the reviewer\u2019s constructive comments, which have helped us improve the clarity, methodological consistency, and scientific interpretation of the manuscript. The corresponding changes can be found in the updated manuscript attached to the email submitted to the Editorial Board.","URL":"https:\/\/f1000research.com\/articles\/15-366\/v2#referee-comment-16907","order":1,"name":"referee-comment-16907","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.195388.r467290, Chedi Morchdi, Texas A&M University, Texas, 21 May 2026, version 1, 1 approved, 1 not approved","URL":"https:\/\/f1000research.com\/articles\/15-366\/v1#referee-response-467290","order":2,"name":"referee-response-467290","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"The author(s) declared that no grants were involved in supporting this work","order":3,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}