{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T14:21:24Z","timestamp":1776781284076,"version":"3.51.2"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T00:00:00Z","timestamp":1724544000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T00:00:00Z","timestamp":1724544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Deanship of Scientific Research, Qassim University","award":["2023-SDG-l-BSRC38266"],"award-info":[{"award-number":["2023-SDG-l-BSRC38266"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s00607-024-01340-8","type":"journal-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T14:01:55Z","timestamp":1724594515000},"page":"3905-3944","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Hybrid deep learning and evolutionary algorithms for accurate cloud workload prediction"],"prefix":"10.1007","volume":"106","author":[{"given":"Tassawar","family":"Ali","sequence":"first","affiliation":[]},{"given":"Hikmat Ullah","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Fawaz Khaled","family":"Alarfaj","sequence":"additional","affiliation":[]},{"given":"Mohammed","family":"AlReshoodi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,25]]},"reference":[{"key":"1340_CR1","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.comcom.2022.10.019","volume":"197","author":"A Javadpour","year":"2023","unstructured":"Javadpour A, Sangaiah AK, Pinto P, Ja\u2019fari F, Zhang W, Majed Hossein Abadi A, Ahmadi HR (2023) An energy-optimized embedded load balancing using DVFS computing in cloud data centers. Comput Commun 197:255\u2013266","journal-title":"Comput Commun"},{"key":"1340_CR2","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.future.2015.02.001","volume":"50","author":"Y Ding","year":"2015","unstructured":"Ding Y, Qin X, Liu L, Wang T (2015) Energy efficient scheduling of virtual machines in cloud with deadline constraint. Futur Gener Comput Syst 50:62\u201374","journal-title":"Futur Gener Comput Syst"},{"key":"1340_CR3","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1016\/j.future.2011.04.017","volume":"28","author":"A Beloglazov","year":"2012","unstructured":"Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Futur Gener Comput Syst 28:755\u2013768","journal-title":"Futur Gener Comput Syst"},{"key":"1340_CR4","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jnlest.2020.100059","volume":"19","author":"ZR Alashhab","year":"2021","unstructured":"Alashhab ZR, Anbar M, Singh MM, Leau YB, Al-Sai ZA, Alhayja\u2019a SA, (2021) Impact of coronavirus pandemic crisis on technologies and cloud computing applications. J Electron Sci Technol 19:25\u201340","journal-title":"J Electron Sci Technol"},{"key":"1340_CR5","unstructured":"Bricher J (2023) Outlook 2023: Technology trends. IFT"},{"key":"1340_CR6","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1109\/ACCESS.2014.2360352","volume":"2","author":"A Ksentini","year":"2014","unstructured":"Ksentini A, Taleb T, Messaoudi F (2014) A LISP-based implementation of follow me cloud. IEEE Access 2:1340\u20131347","journal-title":"IEEE Access"},{"key":"1340_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13677-020-00185-8","volume":"9","author":"D Mytton","year":"2020","unstructured":"Mytton D (2020) Assessing the suitability of the greenhouse gas protocol for calculation of emissions from public cloud computing workloads. J Cloud Comput 9:1\u201311","journal-title":"J Cloud Comput"},{"key":"1340_CR8","doi-asserted-by":"publisher","DOI":"10.2172\/1372902","volume-title":"United States data center energy usage report","author":"A Shehabi","year":"2016","unstructured":"Shehabi A, Smith SJ, Sartor DA, Brown RE, Herrlin M, Koomey JG, Masanet ER, Horner N, Azevedo IL, Lintner W (2016) United States data center energy usage report. Lawrence Berkeley National Lab, Berkeley"},{"key":"1340_CR9","doi-asserted-by":"publisher","first-page":"3963","DOI":"10.1007\/s12652-022-04464-x","volume":"14","author":"A Javadpour","year":"2023","unstructured":"Javadpour A, Nafei AH, Ja\u2019fari F, Pinto P, Zhang W, Sangaiah AK, (2023) An intelligent energy-efficient approach for managing IoE tasks in cloud platforms. J Ambient Intell Humaniz Comput 14:3963\u20133979","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1340_CR10","doi-asserted-by":"publisher","first-page":"2471","DOI":"10.1007\/s11277-020-07691-7","volume":"115","author":"A Javadpour","year":"2020","unstructured":"Javadpour A, Wang G, Rezaei S (2020) Resource management in a peer to peer cloud network for IoT. Wirel Pers Commun 115:2471\u20132488","journal-title":"Wirel Pers Commun"},{"key":"1340_CR11","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/s10922-014-9307-7","volume":"23","author":"B Jennings","year":"2015","unstructured":"Jennings B, Stadler R (2015) Resource management in clouds: survey and research challenges. J Netw Syst Manag 23:567\u2013619","journal-title":"J Netw Syst Manag"},{"key":"1340_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.114806","author":"C Jin","year":"2020","unstructured":"Jin C, Bai X, Yang C, Mao W, Xu X (2020) A review of power consumption models of servers in data centers. Appl Energy. https:\/\/doi.org\/10.1016\/j.apenergy.2020.114806","journal-title":"Appl Energy"},{"key":"1340_CR13","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.ins.2020.07.012","volume":"543","author":"J Kumar","year":"2021","unstructured":"Kumar J, Singh AK, Buyya R (2021) Self directed learning based workload forecasting model for cloud resource management. Inf Sci (Ny) 543:345\u2013366","journal-title":"Inf Sci (Ny)"},{"key":"1340_CR14","first-page":"1","volume-title":"Towards understanding cloud performance tradeoffs using statistical workload analysis and replay","author":"Y Chen","year":"2010","unstructured":"Chen Y, Ganapathi A (2010) Towards understanding cloud performance tradeoffs using statistical workload analysis and replay. University of California, Santa Barbara, pp 1\u201312"},{"key":"1340_CR15","first-page":"33","volume":"11","author":"MA Attia","year":"2019","unstructured":"Attia MA, Arafa M, Sallam EA, Fahmy MM (2019) Application of an enhanced self-adapting differential evolution algorithm to workload prediction in cloud computing. Int J Inf Technol Comput Sci 11:33\u201340","journal-title":"Int J Inf Technol Comput Sci"},{"key":"1340_CR16","doi-asserted-by":"publisher","first-page":"3831","DOI":"10.1016\/j.aej.2021.09.013","volume":"61","author":"MF Ahmad","year":"2022","unstructured":"Ahmad MF, Isa NAM, Lim WH, Ang KM (2022) Differential evolution: a recent review based on state-of-the-art works. Alex Eng J 61:3831\u20133872","journal-title":"Alex Eng J"},{"key":"1340_CR17","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.ijepes.2018.02.021","volume":"100","author":"K Mason","year":"2018","unstructured":"Mason K, Duggan J, Howley E (2018) A multi-objective neural network trained with differential evolution for dynamic economic emission dispatch. Int J Electr Power Energy Syst 100:201\u2013221","journal-title":"Int J Electr Power Energy Syst"},{"key":"1340_CR18","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8682124","author":"Y Tang","year":"2019","unstructured":"Tang Y, Ji J, Zhu Y, Gao S, Tang Z, Todo Y (2019) A Differential evolution-oriented pruning neural network model for bankruptcy prediction. Complexity. https:\/\/doi.org\/10.1155\/2019\/8682124","journal-title":"Complexity"},{"key":"1340_CR19","doi-asserted-by":"publisher","first-page":"124","DOI":"10.15837\/ijccc.2019.1.3420","volume":"14","author":"S Zhang","year":"2019","unstructured":"Zhang S, Chen Y, Huang X, Cai Y (2019) Text classification of public feedbacks using convolutional neural network based on differential evolution algorithm. Int J Comput Commun Control 14:124\u2013134","journal-title":"Int J Comput Commun Control"},{"key":"1340_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41074-019-0053-3","volume":"11","author":"J Su","year":"2019","unstructured":"Su J, Vargas DV, Sakurai K (2019) Attacking convolutional neural network using differential evolution. IPSJ Trans Comput Vis Appl 11:1\u201316","journal-title":"IPSJ Trans Comput Vis Appl"},{"key":"1340_CR21","doi-asserted-by":"publisher","first-page":"4869","DOI":"10.1007\/s10586-018-2409-3","volume":"22","author":"J Li","year":"2019","unstructured":"Li J (2019) Evaluation method based on neural network differential evolution. Cluster Comput 22:4869\u20134875","journal-title":"Cluster Comput"},{"key":"1340_CR22","doi-asserted-by":"publisher","first-page":"12067","DOI":"10.1007\/s00500-019-04647-3","volume":"24","author":"\u00d6F Ertu\u011frul","year":"2020","unstructured":"Ertu\u011frul \u00d6F (2020) A novel clustering method built on random weight artificial neural networks and differential evolution. Soft Comput 24:12067\u201312078","journal-title":"Soft Comput"},{"key":"1340_CR23","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.neucom.2020.02.020","volume":"396","author":"T Huang","year":"2020","unstructured":"Huang T, Duan DT, Gong YJ, Ye L, Ng WWY, Zhang J (2020) Concurrent optimization of multiple base learners in neural network ensembles: an adaptive niching differential evolution approach. Neurocomputing 396:24\u201338","journal-title":"Neurocomputing"},{"key":"1340_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2020.104663","volume":"126","author":"IA Troumbis","year":"2020","unstructured":"Troumbis IA, Tsekouras GE, Tsimikas J, Kalloniatis C, Haralambopoulos D (2020) A Chebyshev polynomial feedforward neural network trained by differential evolution and its application in environmental case studies. Environ Model Softw 126:104663","journal-title":"Environ Model Softw"},{"key":"1340_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2020.104434","volume":"137","author":"R Li","year":"2020","unstructured":"Li R, Zhang H, Zhuang Q, Li R, Chen Y (2020) BP neural network and improved differential evolution for transient electromagnetic inversion. Comput Geosci 137:104434","journal-title":"Comput Geosci"},{"key":"1340_CR26","doi-asserted-by":"publisher","first-page":"8359","DOI":"10.1007\/s11042-023-16008-2","volume":"83","author":"S Mangalampalli","year":"2024","unstructured":"Mangalampalli S, Karri GR, Kumar M, Khalaf OI, Romero CAT, Sahib GMA (2024) DRLBTSA: deep reinforcement learning based task-scheduling algorithm in cloud computing. Multimed Tools Appl 83:8359\u20138387","journal-title":"Multimed Tools Appl"},{"key":"1340_CR27","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TSUSC.2021.3110245","volume":"7","author":"M Kumar","year":"2022","unstructured":"Kumar M, Kishor A, Abawajy J, Agarwal P, Singh A, Zomaya AY (2022) ARPS: an autonomic resource provisioning and scheduling framework for cloud platforms. IEEE Trans Sustain Comput 7:386\u2013399","journal-title":"IEEE Trans Sustain Comput"},{"key":"1340_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7469","author":"M Kumar","year":"2023","unstructured":"Kumar M, Dubey K, Singh S, Kumar Samriya J, Gill SS (2023) Experimental performance analysis of cloud resource allocation framework using spider monkey optimization algorithm. Concurr Comput Pract Exp. https:\/\/doi.org\/10.1002\/cpe.7469","journal-title":"Concurr Comput Pract Exp"},{"key":"1340_CR29","doi-asserted-by":"publisher","first-page":"12103","DOI":"10.1007\/s00521-019-04266-x","volume":"32","author":"M Kumar","year":"2020","unstructured":"Kumar M, Sharma SC (2020) PSO-based novel resource scheduling technique to improve QoS parameters in cloud computing. Neural Comput Appl 32:12103\u201312126","journal-title":"Neural Comput Appl"},{"key":"1340_CR30","doi-asserted-by":"publisher","first-page":"9513","DOI":"10.1109\/JIOT.2023.3235107","volume":"10","author":"M Kumar","year":"2023","unstructured":"Kumar M, Kishor A, Samariya JK, Zomaya AY (2023) An autonomic workload prediction and resource allocation framework for fog-enabled industrial IoT. IEEE Internet Things J 10:9513\u20139522","journal-title":"IEEE Internet Things J"},{"key":"1340_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s10723-021-09559-x","author":"A Shahidinejad","year":"2021","unstructured":"Shahidinejad A, Farahbakhsh F, Ghobaei-Arani M, Malik MH, Anwar T (2021) Context-aware multi-user offloading in mobile edge computing: a federated learning-based approach. J Grid Comput. https:\/\/doi.org\/10.1007\/s10723-021-09559-x","journal-title":"J Grid Comput"},{"key":"1340_CR32","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.1002\/spe.2986","volume":"51","author":"M Salimian","year":"2021","unstructured":"Salimian M, Ghobaei-Arani M, Shahidinejad A (2021) Toward an autonomic approach for Internet of things service placement using gray wolf optimization in the fog computing environment. Softw - Pract Exp 51:1745\u20131772","journal-title":"Softw - Pract Exp"},{"key":"1340_CR33","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1007\/s10586-020-03152-9","volume":"24","author":"M Tarahomi","year":"2021","unstructured":"Tarahomi M, Izadi M, Ghobaei-Arani M (2021) An efficient power-aware VM allocation mechanism in cloud data centers: a micro genetic-based approach. Cluster Comput 24:919\u2013934","journal-title":"Cluster Comput"},{"key":"1340_CR34","doi-asserted-by":"publisher","first-page":"3813","DOI":"10.1007\/s00500-020-05409-2","volume":"25","author":"M Ghobaei-Arani","year":"2021","unstructured":"Ghobaei-Arani M (2021) A workload clustering based resource provisioning mechanism using biogeography based optimization technique in the cloud based systems. Soft Comput 25:3813\u20133830","journal-title":"Soft Comput"},{"key":"1340_CR35","doi-asserted-by":"publisher","first-page":"2603","DOI":"10.1007\/s11227-018-2656-3","volume":"75","author":"M Ghobaei-Arani","year":"2019","unstructured":"Ghobaei-Arani M, Souri A (2019) LP-WSC: a linear programming approach for web service composition in geographically distributed cloud environments. J Supercomput 75:2603\u20132628","journal-title":"J Supercomput"},{"key":"1340_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-19495-z","author":"A Ajil","year":"2024","unstructured":"Ajil A, Kumar ES (2024) IDBNWP: improved deep belief network for workload prediction: hybrid optimization for load balancing in cloud system. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-024-19495-z","journal-title":"Multimed Tools Appl"},{"key":"1340_CR37","doi-asserted-by":"publisher","DOI":"10.1145\/3524114","author":"M Xu","year":"2022","unstructured":"Xu M, Song C, Wu H, Gill SS, Ye K, Xu C (2022) esDNN: deep neural network based multivariate workload prediction in cloud computing environments. ACM Trans Internet Technol. https:\/\/doi.org\/10.1145\/3524114","journal-title":"ACM Trans Internet Technol"},{"key":"1340_CR38","doi-asserted-by":"crossref","unstructured":"Kaim A, Singh S, Patel YS (2023) Ensemble CNN attention-based BiLSTM deep learning architecture for multivariate cloud workload prediction. In: ACM Int Conf Proceeding Ser, pp 342\u2013348","DOI":"10.1145\/3571306.3571433"},{"key":"1340_CR39","doi-asserted-by":"publisher","DOI":"10.1080\/0952813X.2022.2153279","author":"A Abdolmaleki","year":"2022","unstructured":"Abdolmaleki A, Rezvani MH (2022) An optimal context-aware content-based movie recommender system using genetic algorithm: a case study on MovieLens dataset. J Exp Theor Artif Intell. https:\/\/doi.org\/10.1080\/0952813X.2022.2153279","journal-title":"J Exp Theor Artif Intell"},{"key":"1340_CR40","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.future.2023.01.002","volume":"142","author":"YS Patel","year":"2023","unstructured":"Patel YS, Bedi J (2023) MAG-D: a multivariate attention network based approach for cloud workload forecasting. Futur Gener Comput Syst 142:376\u2013392","journal-title":"Futur Gener Comput Syst"},{"key":"1340_CR41","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.future.2017.10.047","volume":"81","author":"J Kumar","year":"2018","unstructured":"Kumar J, Singh AK (2018) Workload prediction in cloud using artificial neural network and adaptive differential evolution. Futur Gener Comput Syst 81:41\u201352","journal-title":"Futur Gener Comput Syst"},{"key":"1340_CR42","doi-asserted-by":"crossref","unstructured":"Beloglazov A, Buyya R (2010) Energy efficient resource management in virtualized cloud data centers. In: CCGrid 2010 - 10th IEEE\/ACM Int Conf Clust Cloud, Grid Comput, pp 826\u2013831","DOI":"10.1109\/CCGRID.2010.46"},{"key":"1340_CR43","doi-asserted-by":"publisher","DOI":"10.1145\/3326285.3329074","author":"J Guo","year":"2019","unstructured":"Guo J, Chang Z, Wang S, Ding H, Feng Y, Mao L, Bao Y (2019) Who limits the resource efficiency of my datacenter: An analysis of Alibaba datacenter traces. Proc Int Symp Qual Serv IWQoS. https:\/\/doi.org\/10.1145\/3326285.3329074","journal-title":"Proc Int Symp Qual Serv IWQoS"},{"key":"1340_CR44","doi-asserted-by":"publisher","first-page":"5481","DOI":"10.5194\/gmd-15-5481-2022","volume":"15","author":"TO Hodson","year":"2022","unstructured":"Hodson TO (2022) Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geosci Model Dev 15:5481\u20135487","journal-title":"Geosci Model Dev"},{"key":"1340_CR45","unstructured":"Miles J (2005) R\u2010Squared, adjusted R\u2010squared. Encycl Stat Behav Sci"},{"key":"1340_CR46","first-page":"2009","volume":"42","author":"E Trivizakis","year":"2019","unstructured":"Trivizakis E, Ioannidis GS, Melissianos VD, Papadakis GZ, Tsatsakis A, Spandidos DA, Marias K (2019) A novel deep learning architecture outperforming \u2018off-the-shelf\u2019 transfer learning and feature-based methods in the automated assessment of mammographic breast density. Oncol Rep 42:2009\u20132015","journal-title":"Oncol Rep"},{"key":"1340_CR47","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/accc08","author":"I Vazquez","year":"2023","unstructured":"Vazquez I, Gronberg MP, Zhang X, Court LE, Zhu XR, Frank SJ, Yang M (2023) A deep learning-based approach for statistical robustness evaluation in proton therapy treatment planning: a feasibility study. Phys Med Biol. https:\/\/doi.org\/10.1088\/1361-6560\/accc08","journal-title":"Phys Med Biol"},{"key":"1340_CR48","doi-asserted-by":"crossref","unstructured":"Cook S (2019) Forecast evaluation using Theil\u2019s inequality coefficients","DOI":"10.53593\/n3168a"},{"key":"1340_CR49","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.neucom.2017.01.064","volume":"238","author":"Q Tian","year":"2017","unstructured":"Tian Q, Chen S (2017) Cross-heterogeneous-database age estimation through correlation representation learning. Neurocomputing 238:286\u2013295","journal-title":"Neurocomputing"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-024-01340-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-024-01340-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-024-01340-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T14:08:07Z","timestamp":1730988487000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-024-01340-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,25]]},"references-count":49,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1340"],"URL":"https:\/\/doi.org\/10.1007\/s00607-024-01340-8","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,25]]},"assertion":[{"value":"8 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interests"}},{"value":"This section is not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}