{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:41:43Z","timestamp":1777250503669,"version":"3.51.4"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"DOI":"10.1186\/s13677-026-00884-8","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:51:14Z","timestamp":1773809474000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-powered dynamic queue optimization in bursty multi traffic environment"],"prefix":"10.1186","volume":"15","author":[{"given":"Tehmina Karamat","family":"Khan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taimur","family":"Karamat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohsan","family":"Tanveer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N. Z.","family":"Jhanjhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sayan Kumar","family":"Ray","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"issue":"3","key":"884_CR1","first-page":"17","volume":"9","author":"S Siddiqui","year":"2020","unstructured":"Siddiqui S, Darbari M, Yagyasen D (2020) Modelling and simulation of queuing models through the concept of petrinets. Adcaij-Adv Distrib Comput Artif Intel J 9(3):17\u201328","journal-title":"Adcaij-Adv Distrib Comput Artif Intel J"},{"issue":"1","key":"884_CR2","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.ejor.2016.07.035","volume":"256","author":"H Baumann","year":"2017","unstructured":"Baumann H, Sandmann W (2017) Multi-server tandem queue with markovian arrival process, phase-type service times, and finite buffers. Eur J Oper Res 256(1):187\u2013195","journal-title":"Eur J Oper Res"},{"key":"884_CR3","doi-asserted-by":"crossref","unstructured":"Niranjan SP, Latha SD, Mahdal M, Karthik K (2024) Multiple control policy in unreliable two-phase bulk queueing system with active bernoulli feedback and vacation. Mathematics 12(1)","DOI":"10.3390\/math12010075"},{"key":"884_CR4","doi-asserted-by":"crossref","unstructured":"Feldmann A, Gilbert AC, Huang P, Willinger W (1999) Dynamics of IP traffic: a study of the role of variability and the impact of control. In Proc. ACM SIGCOMM, pp 301\u2013313","DOI":"10.1145\/316188.316235"},{"key":"884_CR5","doi-asserted-by":"crossref","unstructured":"Sesia S, Toufik I, Baker M (2011) LTE \u2013 the UMTS long term evolution: from theory to practice, 2nd edn. Wiley","DOI":"10.1002\/9780470978504"},{"key":"884_CR6","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V et al. (2015) Human-level control through deep reinforcement learning. Nature 518:529\u2013533","journal-title":"Nature"},{"key":"884_CR7","unstructured":"Leon-Garcia A, Widjaja I (2004) Communication networks: fundamental concepts and key architectures, 2nd edn. McGraw-Hill"},{"issue":"14","key":"884_CR8","doi-asserted-by":"publisher","first-page":"21179","DOI":"10.1007\/s11227-024-06213-7","volume":"80","author":"X Li","year":"2024","unstructured":"Li X, Liu H, Wang H (2024 Jun) Data transmission optimization in edge computing using multi-objective reinforcement learning. J Sport Hist Supercomput 80(14):21179\u201321206. https:\/\/doi.org\/10.1007\/s11227-024-06213-7","journal-title":"J Sport Hist Supercomput"},{"issue":"3","key":"884_CR9","doi-asserted-by":"publisher","first-page":"2951","DOI":"10.1007\/s12652-023-04534-8","volume":"14","author":"PK Donta","year":"2023","unstructured":"Donta PK, Srirama SN, Amgoth T, Annavarapu CSR (2023) iCoCoA: intelligent congestion control algorithm for CoAP using deep reinforcement learning. J Ambient Intell Humaniz Comput 14(3):2951\u20132966","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"2","key":"884_CR10","first-page":"3081","volume":"71","author":"P Tam","year":"2022","unstructured":"Tam P, Math S, Lee A, Kim S (2022) Multi-agent deep Q-Networks for efficient edge federated learning communications in software-defined IoT. Comput Mater Continua 71(2):3081\u20133097","journal-title":"Comput Mater Continua"},{"key":"884_CR11","unstructured":"Tu W (2006) Worst-case delay control in multi-group overlay networks. In Proceedings of the International Conference on Parallel Processing (ICPP), Columbus, OH, USA, 1\u20138"},{"key":"884_CR12","doi-asserted-by":"crossref","unstructured":"Song T, Kyung Y (2024) Deep reinforcement learning based age-of-information-aware low-power active queue management for IoT sensor networks. IEEE internet things J","DOI":"10.1109\/JIOT.2024.3355410"},{"issue":"7","key":"884_CR13","doi-asserted-by":"publisher","first-page":"1246","DOI":"10.1109\/JSAC.2012.120810","volume":"30","author":"W Tu","year":"2012","unstructured":"Tu W (2012 Aug) Efficient resource utilization for multi-flow wireless multicasting transmissions. IEEE J Sel Areas Commun 30(7):1246\u20131258. https:\/\/doi.org\/10.1109\/JSAC.2012.120810","journal-title":"IEEE J Sel Areas Commun"},{"key":"884_CR14","doi-asserted-by":"publisher","unstructured":"Zhou X, Yang J, Li Y, Li S, Su Z (2024 Oct 1) Deep reinforcement learning-based resource scheduling for energy optimization and load balancing in SDN-driven edge computing. Comput Commun 226\u2013227, Art. no. 107925, https:\/\/doi.org\/10.1016\/j.comcom.2024.107925","DOI":"10.1016\/j.comcom.2024.107925"},{"key":"884_CR15","doi-asserted-by":"publisher","unstructured":"Zhang W, Ou H (2025 Nov) Reinforcement learning based multi-objective task scheduling for energy efficient and cost effective cloud edge computing. Sci Rep 15, Art. no. 41716, https:\/\/doi.org\/10.1038\/s41598-025-25666-1","DOI":"10.1038\/s41598-025-25666-1"},{"issue":"1","key":"884_CR16","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1109\/90.554723","volume":"5","author":"W Willinger","year":"1997","unstructured":"Willinger W, Taqqu MS, Sherman R, Wilson DV (1997) Self-similarity through high-variability: statistical analysis of ethernet LAN traffic at the source level. IEEE\/ACM Trans Netw 5(1):71\u201386","journal-title":"IEEE\/ACM Trans Netw"},{"key":"884_CR17","unstructured":"Sutton RS, Barto AG (2018) Reinforcement learning: an introduction, 2nd edn. MIT Press"},{"key":"884_CR18","unstructured":"Kelif JM, Coupechoux M (2011) Performance analysis of voice and data services over 3G\/4G wireless networks. In IEEE PIMRC"},{"issue":"12","key":"884_CR19","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/MCOM.2020.9311928","volume":"58","author":"M Polese","year":"2020","unstructured":"Polese M, Giordani M, Zorzi M (2020) Machine learning at the edge: a game changer for the future evolution of cellular networks. IEEE Commun Mag 58(12):56\u201361","journal-title":"IEEE Commun Mag"},{"key":"884_CR20","doi-asserted-by":"crossref","unstructured":"Liu J, Wei D (2022) Active queue management based on Q-Learning traffic predictor. In Proc. 2022 Int. Conf. Cyber-Physical Social Intelligence (ICCSI), pp 399\u2013404","DOI":"10.1109\/ICCSI55536.2022.9970698"},{"key":"884_CR21","unstructured":"Ismael AA, T\u00fcreli DK (2022 Jun) Study of a smarter AQM algorithm to reduce network delay. AINTELIA Sci Notes J 1, no. 1"},{"issue":"6","key":"884_CR22","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1016\/j.icte.2023.10.005","volume":"9","author":"E-S Jung","year":"2023","unstructured":"Jung E-S, Kim HS (2023) RLECN\u2014A learning based dynamic threshold control of ECN. ICT Express 9(6):1007\u20131012","journal-title":"ICT Express"},{"issue":"3","key":"884_CR23","first-page":"1684","volume":"26","author":"AB Yousif","year":"2022","unstructured":"Yousif AB, Hassan HJ, Muttasher G (2022) Applying reinforcement learning for random early detection algorithm in adaptive queue management systems. Indones J Electr Eng Comput Sci 26(3):1684\u20131691","journal-title":"Indones J Electr Eng Comput Sci"},{"key":"884_CR24","doi-asserted-by":"publisher","first-page":"108811","DOI":"10.1016\/j.compeleceng.2023.108811","volume":"110","author":"MH Ali","year":"2023","unstructured":"Ali MH, \u00d6zt\u00fcrk S (2023) Efficient congestion control in communications using novel weighted ensemble deep reinforcement learning. Comput Electr Eng 110:108811","journal-title":"Comput Electr Eng"},{"key":"884_CR25","doi-asserted-by":"publisher","first-page":"110566","DOI":"10.1016\/j.comnet.2024.110566","volume":"234","author":"C Pan","year":"2024","unstructured":"Pan C, Cui X, Zhao C, Wang Y, Wang Y (2024) An adaptive network congestion control strategy based on the change trend of average queue length. Comput Netw 234:110566","journal-title":"Comput Netw"},{"key":"884_CR26","doi-asserted-by":"crossref","unstructured":"de Almeida LC, da Silva WRD, Tavares TC, Pasquini R, Papagianni C, Verdi FL (2023). DESiRED\u2014dynamic, enhanced, and smart iRED: a P4-AQM with deep reinforcement learning and In-band network telemetry,\u201d arXiv preprint arXiv:2310.18159","DOI":"10.1016\/j.comnet.2024.110326"},{"key":"884_CR27","doi-asserted-by":"publisher","first-page":"108515","DOI":"10.1016\/j.comnet.2021.108515","volume":"200","author":"H Ma","year":"2021","unstructured":"Ma H, Xu D, Dai Y, Dong Q (2021) An intelligent scheme for congestion control: when active queue management meets deep reinforcement learning. Comput Netw 200:108515","journal-title":"Comput Netw"},{"key":"884_CR28","unstructured":"Fawaz H, Zeghlache D, Pham TAQ, Leguay J, Medagliani P (2021) Deep reinforcement learning for smart queue management. Electron Commun EASST 80"},{"key":"884_CR29","doi-asserted-by":"publisher","first-page":"11892","DOI":"10.1109\/ACCESS.2019.2892046","volume":"7","author":"K Xiao","year":"2019","unstructured":"Xiao K, Mao S, Tugnait JK (2019) TCP-Drinc: smart congestion control based on deep reinforcement learning. IEEE Access. 7:11892\u201311904","journal-title":"IEEE Access."},{"key":"884_CR30","doi-asserted-by":"publisher","first-page":"108329","DOI":"10.1016\/j.comnet.2021.108329","volume":"197","author":"Y Lu","year":"2021","unstructured":"Lu Y, Ma X, Xu Z (2021) Choose a correct marking position: ECN should be freed from tail mark. Comput Netw 197:108329","journal-title":"Comput Netw"},{"key":"884_CR31","unstructured":"Mirzaeinnia A, Mirzaeinia M, Rezgui A (2020). Latency and throughput optimization in modern networks: a comprehensive survey,\u201d arXiv preprint arXiv:2009.03715"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-026-00884-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-026-00884-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-026-00884-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:23:25Z","timestamp":1777249405000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13677-026-00884-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["884"],"URL":"https:\/\/doi.org\/10.1186\/s13677-026-00884-8","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,18]]},"assertion":[{"value":"28 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2026","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.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","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":"66"}}