{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:34:11Z","timestamp":1786980851053,"version":"build-2736575974"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10586-023-03999-8","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T12:02:12Z","timestamp":1682683332000},"page":"2789-2800","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Intelligent microservices autoscaling module using reinforcement learning"],"prefix":"10.1007","volume":"26","author":[{"given":"Abeer","family":"Abdel Khaleq","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilkyeun","family":"Ra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"key":"3999_CR1","doi-asserted-by":"publisher","unstructured":"Khaleq, A. A., Ra, I.: Agnostic approach for microservices autoscaling in cloud applications. In :Proc. CSCI. pp. 1411\u20131415. Las Vegas, NV, USA (2019). https:\/\/doi.org\/10.1109\/CSCI49370.2019.00264.","DOI":"10.1109\/CSCI49370.2019.00264."},{"key":"3999_CR2","doi-asserted-by":"crossref","unstructured":"Gan, Y., Delimitrou, C.: The architectural implications of microservices in the cloud, arXiv preprint arXiv:1805.10351Y (2018)","DOI":"10.1109\/LCA.2018.2839189"},{"issue":"1","key":"3999_CR3","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/JAS.2017.7510313","volume":"4","author":"Mohammad Hossein Ghahramani","year":"2017","unstructured":"Ghahramani, Mohammad Hossein, Zhou, MengChu, Hon, Chi Tin: Toward cloud computing QoS architecture: analysis of cloud systems and cloud services. IEEE\/CAA J. Autom. Sinica 4(1), 6\u201318 (2017)","journal-title":"IEEE\/CAA J. Autom. Sinica"},{"issue":"3","key":"3999_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2843889","volume":"48","author":"Sukhpal Singh","year":"2015","unstructured":"Singh, Sukhpal, Chana, Inderveer: QoS-aware autonomic resource management in cloud computing: a systematic review. ACM Comput. Surv. (CSUR) 48(3), 1\u201346 (2015)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"3999_CR5","doi-asserted-by":"crossref","unstructured":"Horovitz, S., Arian, Y.: Efficient cloud auto-scaling with SLA objective using Q-Learning. In: 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud). pp. 85\u201392. IEEE (2018 August)","DOI":"10.1109\/FiCloud.2018.00020"},{"key":"3999_CR6","doi-asserted-by":"crossref","unstructured":"Yang, Z., Nguyen, P., Jin, H., Nahrstedt, K.: MIRAS: Model-based reinforcement learning for microservice resource allocation over scientific workflows. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). pp. 122\u2013132. Dallas, TX, USA (2019)","DOI":"10.1109\/ICDCS.2019.00021"},{"issue":"12","key":"3999_CR7","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1002\/cpe.2864","volume":"25","author":"Enda Barrett","year":"2013","unstructured":"Barrett, Enda, Howley, Enda, Duggan, Jim: Applying reinforcement learning towards automating resource allocation and application scalability in the cloud. Concurr. Comput.: Pract. Exp. 25(12), 1656\u20131674 (2013)","journal-title":"Concurr. Comput.: Pract. Exp."},{"issue":"1","key":"3999_CR8","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s11280-018-0562-5","volume":"22","author":"T Zheng","year":"2019","unstructured":"Zheng, T., Zheng, X., Zhang, Y., Deng, Y., Dong, E., Zhang, R., Liu, X.: SmartVM: a SLA-aware microservice deployment framework. World Wide Web 22(1), 275\u2013293 (2019)","journal-title":"World Wide Web"},{"key":"3999_CR9","first-page":"30","volume-title":"Auto-scaling Policies to Adapt the application deployment in kubernetes","author":"F Rossi","year":"2020","unstructured":"Rossi, F.: Auto-scaling Policies to Adapt the application deployment in kubernetes, pp. 30\u201338. ZEUS, Olympia (2020)"},{"key":"3999_CR10","doi-asserted-by":"crossref","unstructured":"Rossi, F., Nardelli, M., Cardellini, V.: Horizontal and vertical scaling of container-based applications using reinforcement learning. In: Proc. IEEE CLOUD. pp. 329\u2013338. (2019)","DOI":"10.1109\/CLOUD.2019.00061"},{"key":"3999_CR11","doi-asserted-by":"publisher","unstructured":"Jamshidi, P., Sharifloo, A. M., Pahl, C., Metzger, A., Estrada, G.: Self-learning cloud controllers: fuzzy q-learning for knowledge evolution. In: International Conference on Cloud and Autonomic Computing. pp. 208\u2013211. Boston, MA, USA (2015) https:\/\/doi.org\/10.1109\/ICCAC.2015.35","DOI":"10.1109\/ICCAC.2015.35"},{"key":"3999_CR12","doi-asserted-by":"crossref","unstructured":"Rzadca, K., Findeisen, P., Swiderski, J., Zych, P., Broniek, P., Kusmierek, J., Nowak, P., et al.: Autopilot: workload autoscaling at Google. In: Proc. of the Fifteenth European Conference on Computer Systems. pp. 1\u201316. (2020)","DOI":"10.1145\/3342195.3387524"},{"key":"3999_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3061890","author":"AA Khaleq","year":"2021","unstructured":"Khaleq, A.A., Ra, I.: Intelligent autoscaling of microservices in the cloud for real-time applications. IEEE Access (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3061890","journal-title":"IEEE Access"},{"key":"3999_CR14","unstructured":"Train a deep Q-Network with TF-Agents. The TF-Agents Authors. https:\/\/www.tensorflow.org\/agents\/tutorials\/1_dqn_tutorial. Last Accessed 17 Mar 2021"},{"key":"3999_CR15","doi-asserted-by":"crossref","unstructured":"Abdel Khaleq, A., Ra, I.: 20018, November. Twitter analytics for disaster relevance and disaster phase discovery. In: Proceeding of the Future Technologies conference. pp. 401\u2013417. Springer, Cham","DOI":"10.1007\/978-3-030-02686-8_31"},{"key":"3999_CR16","doi-asserted-by":"crossref","unstructured":"Khaleq, A.A., Ra, I.: Cloud-based disaster management as a service A microservice approach for hurricane Twitter data analysis. In: IEEE Global Humanitarian Technology Conference (GHTC). San Jose, CA 2018. 1\u20138 (2018)","DOI":"10.1109\/GHTC.2018.8601887"},{"key":"3999_CR17","unstructured":"Multi-layer perceptron classifier, Scikit Learn. https:\/\/scikit-learn.org\/stable\/modules\/neural_networks_supervised.html. Last Accessed 17 Mar 2021"},{"key":"3999_CR18","doi-asserted-by":"crossref","unstructured":"Imran, M., Elbassuoni S., Castillo, C., Diaz, F., Meier, P.: Practical extraction of disaster-relevant information from social media. In: 22nd International Conference on World Wide Web. pp. 1021\u20131024. ACM, Rio de Janeiro, Brazil (2013)","DOI":"10.1145\/2487788.2488109"},{"key":"3999_CR19","unstructured":"Vertical pod autoscaling, Google cloud. https:\/\/cloud.google.com\/kubernetes-engine\/docs\/concepts\/verticalpodautoscaler. Last Accessed 17 Mar 2021"},{"key":"3999_CR20","unstructured":"Best practices for running cost-optimized Kubernetes applications on GKE, Cloud Architecture Center. https:\/\/cloud.google.com\/solutions\/best-practices-for-running-cost-effective-kubernetes-applications-on-gke. Last Accessed 21 Mar 2021"},{"key":"3999_CR21","unstructured":"Cloud computing QoS for real-time data applications, European commission. https:\/\/cordis.europa.eu\/article\/id\/124056-cloud-computing-qos-for-realtime-data-applications. Last Accessed 15 Apr 2021"},{"key":"3999_CR22","doi-asserted-by":"crossref","unstructured":"Kho Lin, S. et al.: Auto-scaling a defence application across the cloud using Docker and Kubernetes. In: 2018 IEEE\/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion). Zurich, pp. 327\u2013334 (2018)","DOI":"10.1109\/UCC-Companion.2018.00076"},{"key":"3999_CR23","first-page":"599","volume-title":"Adaptive AI-based auto-scaling for Kubernetes","author":"L Toka","year":"2020","unstructured":"Toka, L., Dobreff, G., Fodor, B., Sonkoly, B.: Adaptive AI-based auto-scaling for Kubernetes, p. 599. IEEE, Piscataway (2020)"},{"issue":"6","key":"3999_CR24","doi-asserted-by":"publisher","DOI":"10.1002\/nem.2176","volume":"31","author":"Do-Young Lee","year":"2021","unstructured":"Lee, Do-Young., et al.: Deep Q-network-based auto scaling for service in a multi-access edge computing environment. Int. J. Netw. Manag. 31(6), e2176 (2021)","journal-title":"Int. J. Netw. Manag."},{"key":"3999_CR25","doi-asserted-by":"crossref","unstructured":"Kwan, Anthony, et al.: Hyscale: Hybrid and network scaling of dockerized microservices in cloud data centres. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, (2019)","DOI":"10.1109\/ICDCS.2019.00017"},{"key":"3999_CR26","unstructured":"Khaleq, Abeer Abdel: Design and Evaluation of QoS Aware Intelligent Autoscaling Services for Microservices in Cloud Computing Environments. University of Colorado at Denver, PhD diss. (2021)"},{"key":"3999_CR27","unstructured":"Sachidananda, Vighnesh, Sivaraman, Anirudh: Learned Autoscaling for Cloud Microservices with Multi-Armed Bandits. arXiv preprint arXiv:2112.14845 (2021)"},{"key":"3999_CR28","doi-asserted-by":"crossref","unstructured":"Wu, Qiang, et al.: Dynamically adjusting scale of a kubernetes cluster under qos guarantee. In: 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, (2019)","DOI":"10.1109\/ICPADS47876.2019.00037"},{"key":"3999_CR29","unstructured":"Kingma, Diederik P., Ba, Jimmy: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"3999_CR30","doi-asserted-by":"publisher","unstructured":"Khaleq, A. A., Ra, I.: Development of QoS-aware agents with reinforcement learning for autoscaling of microservices on the cloud. In: IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). pp. 13\u201319. DC, USA (2021) https:\/\/doi.org\/10.1109\/ACSOS-C52956.2021.00025","DOI":"10.1109\/ACSOS-C52956.2021.00025"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-023-03999-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-023-03999-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-023-03999-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T16:26:14Z","timestamp":1693067174000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-023-03999-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,28]]},"references-count":30,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["3999"],"URL":"https:\/\/doi.org\/10.1007\/s10586-023-03999-8","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,28]]},"assertion":[{"value":"14 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have not disclosed any competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}