{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:45:03Z","timestamp":1785415503092,"version":"3.56.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Resource management in computing presents significant challenges that require innovative solutions. This paper proposes a novel architecture integrating artificial intelligence (AI) with model order reduction (ROM) and advanced queueing theory models to enhance resource allocation and task scheduling efficiency. The study demonstrates substantial improvements in critical performance parameters, including response time optimization, resource utilization, and energy consumption management through comprehensive mathematical modeling and machine learning frameworks. The methodology incorporates predictive analytics for resource demand forecasting, intelligent scheduling algorithms for automatic workload adaptation, and calendar queueing techniques for real-time decision-making. Extensive simulations and analyses across multiple queueing scenarios validate the theoretical framework, establishing a robust foundation for efficient computation in large-scale distributed environments. Results indicate a 50% reduction in response time, a 50% increase in throughput, and a 15% improvement in resource utilization. The ROM implementation achieved a 65\u201380% reduction in processing overhead while maintaining 95\u201398% accuracy compared to full-scale models. Energy efficiency improved by 20% through intelligent workload distribution, with system reliability reaching 99.99% uptime. This research contributes to computing advancement by demonstrating the effectiveness of integrating AI-driven resource management with traditional queueing theory, providing a scalable solution for modern infrastructure optimization. The proposed framework\u2019s ability to automatically adapt to varying workloads while maintaining optimal performance parameters represents a significant step forward in resource management technology.<\/jats:p>","DOI":"10.1007\/s10791-025-09581-7","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T12:48:35Z","timestamp":1746794915000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Advanced queueing and scheduling techniques in cloud computing using AI-based model order reduction"],"prefix":"10.1007","volume":"28","author":[{"given":"Himani","family":"Chaudhary","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geetanjali","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinesh Kumar","family":"Nishad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saifullah","family":"Khalid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"9581_CR1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1631\/1\/012155","author":"X Huang","year":"2020","unstructured":"Huang X, Wu D, Zhao N. Study of performance measures and energy consumption for computing centers based on queueing theory. J Phys: Conf Ser. 2020. https:\/\/doi.org\/10.1088\/1742-6596\/1631\/1\/012155.","journal-title":"J Phys: Conf Ser"},{"key":"9581_CR2","doi-asserted-by":"publisher","DOI":"10.1109\/ICECCME55909.2022.9988250","author":"GS Kuaban","year":"2022","unstructured":"Kuaban GS, Soodan B, Kumar R, Czekalski P. A queueing-theoretic analysis of the performance of a computing infrastructure: accounting for task reneging or dropping. IEEE ICECCME. 2022. https:\/\/doi.org\/10.1109\/ICECCME55909.2022.9988250.","journal-title":"IEEE ICECCME"},{"key":"9581_CR3","doi-asserted-by":"publisher","DOI":"10.13052\/jicts2245-800x.1113","author":"X Li","year":"2023","unstructured":"Li X. An IFWA-BSA based approach for task scheduling in computing. J Information Commun Technol. 2023. https:\/\/doi.org\/10.13052\/jicts2245-800x.1113.","journal-title":"J Information Commun Technol"},{"key":"9581_CR4","doi-asserted-by":"publisher","DOI":"10.23919\/ICACT53585.2022.9728946","author":"TC Hung","year":"2022","unstructured":"Hung TC, Tien TD, Hieu LN. A proposed load balancer using na\u00efve bayes to enhance response time on computing. ICACT. 2022. https:\/\/doi.org\/10.23919\/ICACT53585.2022.9728946.","journal-title":"ICACT"},{"key":"9581_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/ESCI53509.2022.9758276","author":"K Dev","year":"2022","unstructured":"Dev K, Patra S, Rout S, Behera S, Sahoo B, Barik RK. Optimizing VM allocation with queue dependent requests in fog network. IEEE ESCI. 2022. https:\/\/doi.org\/10.1109\/ESCI53509.2022.9758276.","journal-title":"IEEE ESCI"},{"key":"9581_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/icaibd55127.2022.9820003","author":"Y Wu","year":"2022","unstructured":"Wu Y, Gao M, Wang Y, Duan L. Deep reinforcement learning scheduling of container workflow considering invalid time-consuming and reliability. IEEE ICAIBD. 2022. https:\/\/doi.org\/10.1109\/icaibd55127.2022.9820003.","journal-title":"IEEE ICAIBD"},{"key":"9581_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2021.102202","volume":"72","author":"T Zhou","year":"2021","unstructured":"Zhou T, Tang D, Zhu H, Zhang Z. Multi-agent reinforcement learning for online scheduling in smart factories. Robot Comput Integrated Manufact. 2021;72: 102202. https:\/\/doi.org\/10.1016\/j.rcim.2021.102202.","journal-title":"Robot Comput Integrated Manufact"},{"key":"9581_CR8","doi-asserted-by":"publisher","DOI":"10.2174\/0123520965255860231012020315","author":"S Shakya","year":"2023","unstructured":"Shakya S, Tripathi P. An evolutionary review on resource scheduling algorithms used for computing with IoT network recent. Adv Electrical Electron Eng. 2023. https:\/\/doi.org\/10.2174\/0123520965255860231012020315.","journal-title":"Adv Electrical Electron Eng"},{"key":"9581_CR9","doi-asserted-by":"publisher","DOI":"10.5815\/ijcnis.2023.05.02","author":"R Alguliyev","year":"2023","unstructured":"Alguliyev R, Alakbarov R. Integer programming models for task scheduling and resource allocation in mobile computing. Int J Comput Netw Information Security. 2023. https:\/\/doi.org\/10.5815\/ijcnis.2023.05.02.","journal-title":"Int J Comput Netw Information Security"},{"key":"9581_CR10","doi-asserted-by":"publisher","DOI":"10.4108\/eetsis.4042","author":"S Potluri","year":"2023","unstructured":"Potluri S, Hamad AA, Godavarthi D, Basa SS. Enhanced task scheduling using optimized particle swarm optimization algorithm in computing environment. EAI Innov Res. 2023. https:\/\/doi.org\/10.4108\/eetsis.4042.","journal-title":"EAI Innov Res"},{"key":"9581_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/ICMTIM58873.2023.10246611","author":"M Yang","year":"2023","unstructured":"Yang M, Zhang X, Guo R, Zhao S, Wang W. Research on intelligent scheduling method of multi collaborative computing network fusion resources. 4th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM). 2023. https:\/\/doi.org\/10.1109\/ICMTIM58873.2023.10246611.","journal-title":"4th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)"},{"key":"9581_CR12","doi-asserted-by":"publisher","DOI":"10.11591\/eei.v12i2.4524","author":"N Ghazy","year":"2023","unstructured":"Ghazy N, Abdelkader A, Zaki M, Eldahshan K. An ameliorated Round Robin algorithm in the computing for task scheduling. Bull Electrical Eng Informatics. 2023. https:\/\/doi.org\/10.11591\/eei.v12i2.4524.","journal-title":"Bull Electrical Eng Informatics"},{"key":"9581_CR13","doi-asserted-by":"publisher","DOI":"10.13052\/jicts2245-800x.1113","author":"X Li","year":"2023","unstructured":"Li X. An IFWA-BSA based approach for task scheduling in computing. J ICT Standard. 2023. https:\/\/doi.org\/10.13052\/jicts2245-800x.1113.","journal-title":"J ICT Standard"},{"issue":"3","key":"9581_CR14","doi-asserted-by":"publisher","first-page":"629","DOI":"10.3390\/electronics13030629","volume":"13","author":"L Zheng","year":"2024","unstructured":"Zheng L, Wei G, Zhang K, Chu H. Traffic classification and packet scheduling strategy with deadline constraints for input-queued switches in time-sensitive networking. Electronics. 2024;13(3):629. https:\/\/doi.org\/10.3390\/electronics13030629.","journal-title":"Electronics"},{"key":"9581_CR15","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1145\/3629104.3666038","volume":"2","author":"AV Goponenko","year":"2024","unstructured":"Goponenko AV, Lamar K, Allan BA, Brandt JM, Dechev D. Job scheduling for HPC clusters: constraint programming vs. backfilling approaches. DEBS Proc. 2024;2:135\u201346. https:\/\/doi.org\/10.1145\/3629104.3666038.","journal-title":"DEBS Proc"},{"key":"9581_CR16","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-934310\/v1","author":"A Hussain","year":"2021","unstructured":"Hussain A, Al-turjman F. Applying task scheduling for IOMT- business optimisation using AI. Res Square. 2021. https:\/\/doi.org\/10.21203\/rs.3.rs-934310\/v1.","journal-title":"Res Square"},{"key":"9581_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/DASC-PICom-CBDCom-CyberSciTech52372.2021.00112","author":"L Zeng","year":"2021","unstructured":"Zeng L, Sun J, Ma J, Liu Q. Task scheduling based on multi-level hashing and HRRN in computing. IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC\/PiCom\/CBDCom\/CyberSciTech. 2021. https:\/\/doi.org\/10.1109\/DASC-PICom-CBDCom-CyberSciTech52372.2021.00112.","journal-title":"IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC\/PiCom\/CBDCom\/CyberSciTech"},{"key":"9581_CR18","doi-asserted-by":"publisher","first-page":"1200576","DOI":"10.3389\/fnins.2023.1200576","volume":"17","author":"J Wang","year":"2023","unstructured":"Wang J, Zhang X, Chen X, Song Z. A touch-free human-robot collaborative surgical navigation robotic system based on hand gesture recognition. Front Neurosci. 2023;17:1200576.","journal-title":"Front Neurosci"},{"issue":"17","key":"9581_CR19","doi-asserted-by":"publisher","first-page":"9690","DOI":"10.3390\/app13179690","volume":"13","author":"J Jeong","year":"2023","unstructured":"Jeong J, Jeong J. Factory simulation of optimization techniques based on deep reinforcement learning for storage devices. Appl Sci. 2023;13(17):9690.","journal-title":"Appl Sci"},{"issue":"18","key":"9581_CR20","doi-asserted-by":"publisher","first-page":"4686","DOI":"10.3390\/en17184686","volume":"17","author":"C Ji","year":"2024","unstructured":"Ji C, Ghorbani R. Reliable energy optimization strategy for fuel cell hybrid electric vehicles considering fuel cell and battery health. Energies. 2024;17(18):4686.","journal-title":"Energies"},{"issue":"17","key":"9581_CR21","doi-asserted-by":"publisher","first-page":"8466","DOI":"10.3390\/app12178466","volume":"12","author":"Y Jin","year":"2022","unstructured":"Jin Y, Jin Y, Wen S, Wen S, Shi Z, Li H. Target recognition and navigation path optimization based on NAO robot. Appl Sci. 2022;12(17):8466.","journal-title":"Appl Sci"},{"key":"9581_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2024.100661","volume":"53","author":"S Kumar","year":"2024","unstructured":"Kumar S, Dwivedi M, Kumar M, Gill SS. A comprehensive review of vulnerabilities and AI-enabled defense against DDoS attacks for securing services. Comput Sci Rev. 2024;53: 100661. https:\/\/doi.org\/10.1016\/j.cosrev.2024.100661.","journal-title":"Comput Sci Rev"},{"issue":"1","key":"9581_CR23","doi-asserted-by":"publisher","first-page":"2311","DOI":"10.1109\/tce.2023.3347690","volume":"70","author":"JK Samriya","year":"2023","unstructured":"Samriya JK, Kumar S, Kumar M, Xu M, Wu H, Gill SS. Blockchain and reinforcement neural network for trusted -Enabled IoT network. IEEE Trans Consum Electron. 2023;70(1):2311\u201322. https:\/\/doi.org\/10.1109\/tce.2023.3347690.","journal-title":"IEEE Trans Consum Electron"},{"issue":"5","key":"9581_CR24","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.47974\/jdmsc-1770","volume":"26","author":"D Shetty","year":"2023","unstructured":"Shetty D, Gangadharan SMP, Arumugam K, Islam S, Sagar KVD, Cotrina-Aliaga JC, Kumar S. A holistic mathematical cyber security model in fog resource management in computing environments. J Discret Math Sci Cryptogr. 2023;26(5):1447\u201356. https:\/\/doi.org\/10.47974\/jdmsc-1770.","journal-title":"J Discret Math Sci Cryptogr"},{"issue":"1","key":"9581_CR25","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s10586-024-04686-y","volume":"28","author":"SS Gill","year":"2024","unstructured":"Gill SS, Golec M, Hu J, Xu M, Du J, Wu H, Walia GK, Murugesan SS, Ali B, Kumar M, Ye K, Verma P, Kumar S, Cuadrado F, Uhlig S. Edge AI: a taxonomy, systematic review and future directions. Cluster Comput. 2024;28(1):18. https:\/\/doi.org\/10.1007\/s10586-024-04686-y.","journal-title":"Cluster Comput"},{"issue":"1","key":"9581_CR26","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/jimaging9010010","volume":"9","author":"AS Yadav","year":"2022","unstructured":"Yadav AS, Kumar S, Karetla GR, Cotrina-Aliaga JC, Arias-Gonz\u00e1les JL, Kumar V, Srivastava S, Gupta R, Ibrahim S, Paul R, Naik N, Singla B, Tatkar NS. A feature extraction using probabilistic neural network and BTFSC-Net model with deep learning for brain tumor classification. J Imaging. 2022;9(1):10. https:\/\/doi.org\/10.3390\/jimaging9010010.","journal-title":"J Imaging"},{"key":"9581_CR27","doi-asserted-by":"publisher","DOI":"10.30919\/es933","author":"N Goswami","year":"2023","unstructured":"Goswami N, Raj S, Thakral D, Arias-Gonz\u00e1les JL, Flores-Albornoz J, Asnate-Salazar E, Kapila D, Yadav S, Kumar S. Intrusion detection system for IoT-based healthcare intrusions with Lion-Salp-Swarm-optimization algorithm: metaheuristic-enabled hybrid intelligent approach. Eng Sci. 2023. https:\/\/doi.org\/10.30919\/es933.","journal-title":"Eng Sci"},{"issue":"1","key":"9581_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/ijcac.311033","volume":"12","author":"S Kumar","year":"2022","unstructured":"Kumar S, Kumar S, Ranjan N, Tiwari S, Kumar TR, Goyal D, Sharma G, Arya V, Rafsanjani MK. Digital watermarking-based cryptosystem for resource provisioning. Int J Appl Comput. 2022;12(1):1\u201320. https:\/\/doi.org\/10.4018\/ijcac.311033.","journal-title":"Int J Appl Comput"},{"issue":"5","key":"9581_CR29","doi-asserted-by":"publisher","first-page":"2297","DOI":"10.1007\/s41870-022-00989-8","volume":"14","author":"S Kumar","year":"2022","unstructured":"Kumar S, Samriya JK, Yadav AS, Kumar M. To improve scalability with Boolean matrix using efficient gossip failure detection and consensus algorithm for PeerSim simulator in IoT environment. Int J Inf Technol. 2022;14(5):2297\u2013307. https:\/\/doi.org\/10.1007\/s41870-022-00989-8.","journal-title":"Int J Inf Technol"},{"key":"9581_CR30","doi-asserted-by":"publisher","first-page":"2641","DOI":"10.1038\/s41598-025-85393-5","volume":"15","author":"DK Nishad","year":"2025","unstructured":"Nishad DK, Tiwari AN, Khalid S, et al. AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimization. Sci Rep. 2025;15:2641. https:\/\/doi.org\/10.1038\/s41598-025-85393-5.","journal-title":"Sci Rep"},{"key":"9581_CR31","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1038\/s41598-024-84441-w","volume":"15","author":"VR Verma","year":"2025","unstructured":"Verma VR, et al. Quantum machine learning for Lyapunov-stabilized computation offloading in next-generation MEC networks. Sci Rep. 2025;15:405. https:\/\/doi.org\/10.1038\/s41598-024-84441-w.","journal-title":"Sci Rep"},{"key":"9581_CR32","doi-asserted-by":"publisher","first-page":"3759","DOI":"10.1038\/s41598-025-87866-z","volume":"15","author":"A Singh","year":"2025","unstructured":"Singh A, Yadav S, Tiwari N, et al. Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization. Sci Rep. 2025;15:3759. https:\/\/doi.org\/10.1038\/s41598-025-87866-z.","journal-title":"Sci Rep"},{"key":"9581_CR33","doi-asserted-by":"publisher","first-page":"17935","DOI":"10.1038\/s41598-024-68575-5","volume":"14","author":"DK Nishad","year":"2024","unstructured":"Nishad DK, Tiwari AN, Khalid S, et al. AI-based UPQC control technique for power quality optimization of railway transportation systems. Sci Rep. 2024;14:17935. https:\/\/doi.org\/10.1038\/s41598-024-68575-5.","journal-title":"Sci Rep"},{"key":"9581_CR34","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s42452-024-06372-5","volume":"6","author":"DK Nishad","year":"2024","unstructured":"Nishad DK, Tiwari AN, Khalid S, et al. Power quality solutions for rail transport using AI-based unified power quality conditioners. Discov Appl Sci. 2024;6:651. https:\/\/doi.org\/10.1007\/s42452-024-06372-5.","journal-title":"Discov Appl Sci"},{"key":"9581_CR35","doi-asserted-by":"publisher","DOI":"10.5772\/intechopen.1008531","author":"A Soofastaei","year":"2025","unstructured":"Soofastaei A. Intelligent scheduling: how AI and advanced analytics are revolutionizing time optimization. In IntechOpen eBooks. 2025. https:\/\/doi.org\/10.5772\/intechopen.1008531.","journal-title":"In IntechOpen eBooks"},{"key":"9581_CR36","doi-asserted-by":"publisher","unstructured":"Stiliadis D, Varma A. Design and analysis of frame-based fair queueing. SIGMETRICS \u201996: Proceedings of the 1996 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer System. 1996. https:\/\/doi.org\/10.1145\/233013.233030","DOI":"10.1145\/233013.233030"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09581-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09581-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09581-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T12:48:35Z","timestamp":1746794915000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09581-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,9]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9581"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09581-7","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,9]]},"assertion":[{"value":"20 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable as this study does not involve human subjects or animal experiments.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable as this manuscript does not contain any individual person\u2019s data in any form.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"75"}}