{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T07:13:04Z","timestamp":1777965184177,"version":"3.51.4"},"reference-count":76,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:00:00Z","timestamp":1777852800000},"content-version":"vor","delay-in-days":123,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>The increasing complexity and dynamic nature of workloads in cloud computing are characterized by various patterns with time\u2010dependent features. Such variations pose considerable challenges on Cloud Service Providers (CSPs) incurred by the fluctuation of resource demands along with their impact on quality of service (QoS) optimization. The Google cluster trace is an example of such workloads wherein task characteristics significantly differ between applications, which makes the existing approaches insufficient for accurate predictions across various workload scenarios. Such limitations emphasize the need for a comprehensive, domain\u2010specific consideration of the application of deep learning (DL) models to effectively predict cloud workloads. This paper investigates the design and application of a workload prediction model based on long short\u2010term memory (LSTM) networks, developed for the unique characteristics of the Google cluster trace. The model leverages the LSTM\u2019s ability in capturing long\u2010term dependencies within the trace to predict future workload demands, with the extracted features fed into various machine learning (ML) models such as linear regression (LR), K\u2010nearest neighbor (KNN), and decision tree (DT). The prediction model enables CSPs to proactively allocate sufficient resources for client tasks and optimize workload distribution. It is found that the proposed model combines strengths of feature extraction in LSTM with the prediction\u2019s accuracy of LR to forecast CPU and memory requests of the Google cluster trace. This combination demonstrates superior capabilities compared to convolutional neural network (CNN), autoregressive integrated moving average (ARIMA), and extreme gradient boosting (XGBoost) models.<\/jats:p>","DOI":"10.1155\/acis\/1160180","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T06:53:52Z","timestamp":1777964032000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An LSTM\u2010Based Resource Prediction Model in Google Cloud Data Center"],"prefix":"10.1155","volume":"2026","author":[{"given":"Eman","family":"Alshboul","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3805-7603","authenticated-orcid":false,"given":"Husam","family":"Suleiman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3603-1729","authenticated-orcid":false,"given":"Mohammad","family":"Alshboul","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,5,4]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"crossref","DOI":"10.1177\/18479790221093992","article-title":"Adoption of Cloud Computing as Innovation in the Organization","author":"Golightly L.","year":"2022","journal-title":"International Journal of Engineering Business Management"},{"key":"e_1_2_11_2_2","unstructured":"SaxenaD.andSinghA. Workload Forecasting and Resource Management Models Based on Machine Learning for Cloud Computing Environments."},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/1843671"},{"key":"e_1_2_11_4_2","unstructured":"MellP.andGranceT. The NIST Definition of Cloud Computing."},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-017-0925-7"},{"key":"e_1_2_11_6_2","volume-title":"Cloud Computing Models","author":"Gorelik E.","year":"2013"},{"key":"e_1_2_11_7_2","doi-asserted-by":"crossref","unstructured":"MaoY. RenD. andChenX. Adaptive Load Balancing Algorithm Based on Prediction Model in Cloud Computing Proceedings of the Second International Conference on Innovative Computing and Cloud Computing 2013 165\u2013170.","DOI":"10.1145\/2556871.2556907"},{"key":"e_1_2_11_8_2","article-title":"Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions","volume":"2","author":"Iqbal S.","year":"2021","journal-title":"SN Computer Science"},{"key":"e_1_2_11_9_2","first-page":"395","article-title":"Resource Provisioning Techniques in Cloud Computing Environment: A Survey","volume":"3","author":"Bhavani B. H.","year":"2014","journal-title":"International Journal of Research in Computer and Communication Technology"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2017.2753802"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12030650"},{"key":"e_1_2_11_12_2","volume-title":"More Google Cluster Data","author":"Wilkes J.","year":"2011"},{"key":"e_1_2_11_13_2","first-page":"1","volume-title":"Google Cluster-Usage Traces: Format+ Schema","author":"Reiss C.","year":"2011"},{"key":"e_1_2_11_14_2","doi-asserted-by":"crossref","unstructured":"ReissC. TumanovA. GangerG. KatzR. andKozuchM. Heterogeneity and Dynamicity of Clouds at Scale: Google Trace Analysis ACM Symposium on Cloud Computing (SoCC) 2012.","DOI":"10.1145\/2391229.2391236"},{"key":"e_1_2_11_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-019-03010-3"},{"key":"e_1_2_11_16_2","unstructured":"TabashM. Workload Characterization and Autoscaling in Cloud Environments."},{"key":"e_1_2_11_17_2","doi-asserted-by":"crossref","unstructured":"LiuB. LinY. andChenY. Quantitative Workload Analysis and Prediction Using Google Cluster Traces 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) 2016 IEEE 935\u2013940.","DOI":"10.1109\/INFCOMW.2016.7562213"},{"key":"e_1_2_11_18_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.5120\/ijca2016907483","article-title":"SLA Violation Detection Mechanism for Cloud Computing","volume":"133","author":"Musa S.","year":"2016","journal-title":"International Journal of Computer Application"},{"key":"e_1_2_11_19_2","first-page":"272","volume-title":"WEBIST","author":"Saidi R.","year":"2022"},{"key":"e_1_2_11_20_2","doi-asserted-by":"publisher","DOI":"10.1155\/acis\/7322398"},{"key":"e_1_2_11_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100379"},{"key":"e_1_2_11_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gltp.2021.01.004"},{"key":"e_1_2_11_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3234150"},{"key":"e_1_2_11_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3561381"},{"key":"e_1_2_11_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-017-2044-4"},{"key":"e_1_2_11_26_2","doi-asserted-by":"crossref","unstructured":"DangM.andYooM. A Web Application Load Prediction Model Using Recurrent Neural Network in Cloud 2020 International Conference on Information and Communication Technology Convergence (ICTC) 2020 IEEE 510\u2013514.","DOI":"10.1109\/ICTC49870.2020.9289256"},{"key":"e_1_2_11_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10512-5"},{"key":"e_1_2_11_28_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_11_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2020.12.1342"},{"key":"e_1_2_11_30_2","doi-asserted-by":"crossref","unstructured":"RehmerA.andKrollA. On Using Gated Recurrent Units for Nonlinear System Identification 2019 18th European Control Conference (ECC) 2019 IEEE 2504\u20132509.","DOI":"10.23919\/ECC.2019.8795631"},{"key":"e_1_2_11_31_2","doi-asserted-by":"crossref","unstructured":"UzairM.andJamilN. Effects of Hidden Layers on the Efficiency of Neural Networks 2020 IEEE 23rd International Multitopic Conference (INMIC) 2020 IEEE 1\u20136.","DOI":"10.1109\/INMIC50486.2020.9318195"},{"key":"e_1_2_11_32_2","first-page":"117","article-title":"Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research","author":"Das S.","year":"2023","journal-title":"Machine Learning for Brain Disorders"},{"key":"e_1_2_11_33_2","doi-asserted-by":"crossref","unstructured":"ZhangJ.andManK. F. Time Series Prediction Using RNN in Multi-Dimension Embedding Phase Space 2 SMC\u201998 Conference Proceedings of 1998 IEEE International Conference on Systems Man and Cybernetics (Cat. No.98CH36218) 1998 1868\u20131873.","DOI":"10.1109\/ICSMC.1998.728168"},{"key":"e_1_2_11_34_2","doi-asserted-by":"crossref","unstructured":"KaurM.andMohtaA. A Review of Deep Learning With Recurrent Neural Network Proceedings of the 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) 2019 460\u2013465.","DOI":"10.1109\/ICSSIT46314.2019.8987837"},{"key":"e_1_2_11_35_2","doi-asserted-by":"publisher","DOI":"10.31449\/inf.v47i10.5268"},{"key":"e_1_2_11_36_2","doi-asserted-by":"publisher","DOI":"10.3390\/pr11051382"},{"key":"e_1_2_11_37_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/1681096"},{"key":"e_1_2_11_38_2","doi-asserted-by":"publisher","DOI":"10.1155\/acis\/7933078"},{"key":"e_1_2_11_39_2","unstructured":"StaudemeyerR.andMorrisE. Understanding LSTM-A Tutorial Into Long Short-Term Memory Recurrent Neural Networks."},{"key":"e_1_2_11_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.01.072"},{"key":"e_1_2_11_41_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01199"},{"key":"e_1_2_11_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.102068"},{"key":"e_1_2_11_43_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6596397"},{"key":"e_1_2_11_44_2","doi-asserted-by":"crossref","unstructured":"PulverA.andLyuS. LSTM With Working Memory Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN) 2017 845\u2013851.","DOI":"10.1109\/IJCNN.2017.7965940"},{"key":"e_1_2_11_45_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00444-8"},{"key":"e_1_2_11_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3376441"},{"key":"e_1_2_11_47_2","doi-asserted-by":"crossref","unstructured":"RoyN. DubeyA. andAniruddhaG. Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting 2011 IEEE 4th International Conference on Cloud Computing 2011 IEEE 500\u2013507.","DOI":"10.1109\/CLOUD.2011.42"},{"key":"e_1_2_11_48_2","doi-asserted-by":"crossref","unstructured":"KarimR. IsmaeelS. andAliM. Energy-Efficient Resource Allocation for Cloud Data Centres Using a Multi-Way Data Analysis Technique Theory Design Development and Practice: 18th International Conference HCI (Human-Computer Interaction) International 2016 2016 Springer 577\u2013585.","DOI":"10.1007\/978-3-319-39510-4_53"},{"key":"e_1_2_11_49_2","doi-asserted-by":"crossref","unstructured":"QaziK. LiY. andSohnA. Workload Prediction of Virtual Machines for Harnessing Data Center Resources 2014 IEEE 7th International Conference on Cloud Computing 2014 IEEE 522\u2013529.","DOI":"10.1109\/CLOUD.2014.76"},{"key":"e_1_2_11_50_2","doi-asserted-by":"crossref","unstructured":"YangJ. LiuC. ShangY. MaoZ. andChenJ. Workload Predicting-Based Automatic Scaling in Service Clouds 2013 IEEE Sixth International Conference on Cloud Computing 2013 IEEE 810\u2013815.","DOI":"10.1109\/CLOUD.2013.146"},{"key":"e_1_2_11_51_2","doi-asserted-by":"crossref","unstructured":"RasheduzzamanM. IslamM. IslamT. HossainT. andRahmanR. Study of Different Forecasting Models on Google Cluster Trace 16th International Conference on Computer and Information Technology 2014 IEEE 414\u2013419.","DOI":"10.1109\/ICCITechn.2014.6997346"},{"key":"e_1_2_11_52_2","doi-asserted-by":"crossref","unstructured":"KumarA.andMazumdarS. Forecasting HPC Workload Using ARMA Models and SSA 2016 International Conference on Information Technology (ICIT) 2016 IEEE 294\u2013297.","DOI":"10.1109\/ICIT.2016.065"},{"key":"e_1_2_11_53_2","doi-asserted-by":"crossref","unstructured":"JhengJ.-J. TsengF.-H. ChaoH.-C. andChouL.-D. A Novel VM Workload Prediction Using Grey Forecasting Model in Cloud Data Center The International Conference on Information Networking 2014 (ICOIN2014) 2014 IEEE 40\u201345.","DOI":"10.1109\/ICOIN.2014.6799662"},{"key":"e_1_2_11_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcc.2014.2350475"},{"key":"e_1_2_11_55_2","unstructured":"LiS. WangY. QiuX. WangD. andWangL. A Workload Prediction-Based Multi-VM Provisioning Mechanism in Cloud Computing 2013 15th Asia-Pacific Network Operations and Management Symposium (APNOMS) 2013 IEEE 1\u20136."},{"key":"e_1_2_11_56_2","doi-asserted-by":"crossref","unstructured":"KhanA. YanX. TaoS. andAnerousisN. Workload Characterization and Prediction in the Cloud: A Multiple Time Series Approach 2012 IEEE Network Operations and Management Symposium 2012 IEEE 1287\u20131294.","DOI":"10.1109\/NOMS.2012.6212065"},{"key":"e_1_2_11_57_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-018-2868-6"},{"key":"e_1_2_11_58_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-2133-3"},{"key":"e_1_2_11_59_2","doi-asserted-by":"crossref","unstructured":"GaoJ. WangH. andShenH. Machine Learning Based Workload Prediction in Cloud Computing 2020 29th International Conference on Computer Communications and Networks (ICCCN) 2020 IEEE 1\u20139.","DOI":"10.1109\/ICCCN49398.2020.9209730"},{"key":"e_1_2_11_60_2","doi-asserted-by":"crossref","unstructured":"KirchoffD. XavierM. MastellaJ. andRoseC. D. A Preliminary Study of Machine Learning Workload Prediction Techniques for Cloud Applications 2019 27th Euromicro International Conference on Parallel Distributed and Network-Based Processing (PDP) 2019 IEEE 222\u2013227.","DOI":"10.1109\/EMPDP.2019.8671604"},{"key":"e_1_2_11_61_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-019-02851-4"},{"key":"e_1_2_11_62_2","doi-asserted-by":"crossref","first-page":"1848","DOI":"10.1109\/TCC.2020.2998017","article-title":"Forecasting Cloud Application Workloads With Cloudinsight for Predictive Resource Management","volume":"10","author":"Kim I.","year":"2020","journal-title":"IEEE Transactions on Cloud Computing"},{"key":"e_1_2_11_63_2","doi-asserted-by":"crossref","unstructured":"YuY. JindalV. YenI.-L. andBastaniF. Integrating Clustering and Learning for Improved Workload Prediction in the Cloud 2016 IEEE 9th International Conference on Cloud Computing (CLOUD) 2016 IEEE 876\u2013879.","DOI":"10.1109\/CLOUD.2016.0127"},{"key":"e_1_2_11_64_2","doi-asserted-by":"crossref","unstructured":"YuY. JindalV. BastaniF. LiF. andYenI.-L. Improving the Smartness of Cloud Management Via Machine Learning Based Workload Prediction 2 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) 2018 IEEE 38\u201344 https:\/\/doi.org\/10.1109\/compsac.2018.10200 2-s2.0-85055564282.","DOI":"10.1109\/COMPSAC.2018.10200"},{"key":"e_1_2_11_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpds.2019.2953745"},{"key":"e_1_2_11_66_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-019-02967-7"},{"key":"e_1_2_11_67_2","doi-asserted-by":"crossref","unstructured":"YadavM. PalN. andYadavD. Workload Prediction Over Cloud Server Using Time Series Data 2021 11th International Conference on Cloud Computing Data Science & Engineering (Confluence) 2021 IEEE 267\u2013272.","DOI":"10.1109\/Confluence51648.2021.9377032"},{"key":"e_1_2_11_68_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-022-01129-7"},{"key":"e_1_2_11_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.12.087"},{"key":"e_1_2_11_70_2","doi-asserted-by":"crossref","unstructured":"BhagtyaP. RaghavanS. andChandraseakranK. Workload Classification in Multi-VM Cloud Environment Using Deep Neural Network Model Proceedings of the 36th Annual ACM Symposium on Applied Computing 2021 79\u201382.","DOI":"10.1145\/3412841.3442068"},{"key":"e_1_2_11_71_2","doi-asserted-by":"crossref","unstructured":"QiuF. ZhangB. andGuoJ. A Deep Learning Approach for VM Workload Prediction in the Cloud 2016 17th IEEE\/ACIS International Conference on Software Engineering Artificial Intelligence Networking and Parallel\/Distributed Computing (SNPD) 2016 IEEE 319\u2013324.","DOI":"10.1109\/SNPD.2016.7515919"},{"key":"e_1_2_11_72_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-020-03214-y"},{"key":"e_1_2_11_73_2","doi-asserted-by":"crossref","unstructured":"YazdanianP.andSharifianS. Cloud Workload Prediction Using Convnet and Stacked LSTM 2018 4th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) 2018 IEEE 83\u201387.","DOI":"10.1109\/ICSPIS.2018.8700546"},{"key":"e_1_2_11_74_2","volume-title":"Google Cluster-Usage Traces v3","author":"Wilkes J.","year":"2020"},{"key":"e_1_2_11_75_2","first-page":"1","article-title":"Data Normalization and Standardization: A Technical Report","volume":"1","author":"Ali P.","year":"2014","journal-title":"Machine Learning Technical Report"},{"key":"e_1_2_11_76_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2649-2"}],"container-title":["Applied Computational Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/acis\/1160180","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/acis\/1160180","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/acis\/1160180","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T06:54:01Z","timestamp":1777964041000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/acis\/1160180"}},"subtitle":[],"editor":[{"given":"Said","family":"El Kafhali","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":76,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1155\/acis\/1160180"],"URL":"https:\/\/doi.org\/10.1155\/acis\/1160180","archive":["Portico"],"relation":{},"ISSN":["1687-9724","1687-9732"],"issn-type":[{"value":"1687-9724","type":"print"},{"value":"1687-9732","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-10-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"1160180"}}