{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:50:54Z","timestamp":1757623854220,"version":"3.44.0"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032020482"},{"type":"electronic","value":"9783032020499"}],"license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-02049-9_20","type":"book-chapter","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T19:51:34Z","timestamp":1755719494000},"page":"260-275","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DInos: A Deep Reinforcement Learning Approach to\u00a0Generalizable Autoscaling in\u00a0Stateless Cloud Applications"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3669-0453","authenticated-orcid":false,"given":"Constantinos","family":"Bitsakos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4420-8949","authenticated-orcid":false,"given":"Dimitrios","family":"Tsoumakos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0352-4988","authenticated-orcid":false,"given":"Ioannis","family":"Konstantinou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4890-8427","authenticated-orcid":false,"given":"Nectarios","family":"Koziris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"20_CR1","unstructured":"Grafana: The open platform for analytics and monitoring (2021). https:\/\/grafana.com. Accessed 21 Oct 2024"},{"key":"20_CR2","unstructured":"Prometheus: Monitoring system and time series database (2021). https:\/\/prometheus.io. Accessed 21 Oct 2024"},{"key":"20_CR3","unstructured":"Cluster autoscaler (2024). https:\/\/github.com\/kubernetes\/autoscaler\/blob\/master\/cluster-autoscaler\/README.md. Accessed 22 Oct 2024"},{"key":"20_CR4","unstructured":"Horizontal pod autoscaler (2024). https:\/\/kubernetes.io\/docs\/tasks\/run-application\/horizontal-pod-autoscale\/. Accessed 22 Oct 2024"},{"key":"20_CR5","unstructured":"Kubernetes metrics server (2024). https:\/\/github.com\/kubernetes-sigs\/metrics-server. Accessed 21 Oct 2024"},{"key":"20_CR6","unstructured":"Vertical pod autoscaler. https:\/\/kubernetes.io\/docs\/concepts\/workloads\/pods\/pod-lifecycle\/#vertical-pod-autoscaling (2024). Accessed 22 Oct 2024"},{"key":"20_CR7","doi-asserted-by":"publisher","unstructured":"Ardagna, C., et\u00a0al.: A competitive scalability approach for cloud architectures. IEEE Trans. Serv. Comput. (2014). https:\/\/doi.org\/10.1109\/TSC.2014.2372786","DOI":"10.1109\/TSC.2014.2372786"},{"key":"20_CR8","unstructured":"Bell-Thomas, A.H.: Exploring variational deep Q networks. arXiv preprint arXiv:2004.05615 (2020). https:\/\/arxiv.org\/abs\/2004.05615"},{"key":"20_CR9","doi-asserted-by":"publisher","unstructured":"Ben\u00a0Seghier, N., Kazar, O.: Performance benchmarking and comparison of NoSQL databases: Redis vs MongoDB vs Cassandra using YCSB tool. IEEE (2021). https:\/\/doi.org\/10.1109\/ICRAMI52622.2021.9585956","DOI":"10.1109\/ICRAMI52622.2021.9585956"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Bitsakos, C., Konstantinou, I., Koziris, N.: Derp: A deep reinforcement learning cloud system for elastic resource provisioning. In: 2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 21\u201329. IEEE (2018)","DOI":"10.1109\/CloudCom2018.2018.00020"},{"key":"20_CR11","doi-asserted-by":"publisher","unstructured":"Dang-Quang, N.M., Yoo, M.: Deep learning-based autoscaling using bidirectional long short-term memory for Kubernetes. Appl. Sci. 11(9) (2021). https:\/\/doi.org\/10.3390\/app11093835","DOI":"10.3390\/app11093835"},{"key":"20_CR12","doi-asserted-by":"publisher","unstructured":"Fogli, M., et\u00a0al.: Performance evaluation of Kubernetes distributions in federated cloud infrastructure. In: IEEE International Conference on Cloud Computing (2021). https:\/\/doi.org\/10.1109\/CLOUD.2021.00073","DOI":"10.1109\/CLOUD.2021.00073"},{"key":"20_CR13","unstructured":"Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: off-policy maximum entropy Deep Reinforcement Learning with a stochastic actor. In: Proceedings of the 35th International Conference on Machine Learning (ICML), pp. 1861\u20131870. JMLR.org (2018)"},{"issue":"1","key":"20_CR14","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1214\/aoms\/1177703732","volume":"35","author":"PJ Huber","year":"1964","unstructured":"Huber, P.J.: Robust estimation of a location parameter. Ann. Math. Stat. 35(1), 73\u2013101 (1964)","journal-title":"Ann. Math. Stat."},{"key":"20_CR15","doi-asserted-by":"publisher","unstructured":"Ikemoto, J., et\u00a0al.: Application of deep reinforcement learning to control problems. In: International Symposium on Control, Automation, and Systems (2019). https:\/\/doi.org\/10.23919\/ICCAS.2019.8912116","DOI":"10.23919\/ICCAS.2019.8912116"},{"issue":"13","key":"20_CR16","doi-asserted-by":"publisher","first-page":"9745","DOI":"10.1007\/s00521-019-04507-z","volume":"32","author":"M Imdoukh","year":"2019","unstructured":"Imdoukh, M., Ahmad, I., Alfailakawi, M.G.: Machine learning-based auto-scaling for containerized applications. Neural Comput. Appl. 32(13), 9745\u20139760 (2019). https:\/\/doi.org\/10.1007\/s00521-019-04507-z","journal-title":"Neural Comput. Appl."},{"key":"20_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/SmartCloud.2017.9","author":"H Kimm","year":"2017","unstructured":"Kimm, H., Li, Z., Kimm, H.: Scadis: supporting reliable scalability in Redis replication on demand. IEEE (2017). https:\/\/doi.org\/10.1109\/SmartCloud.2017.9","journal-title":"IEEE"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Koukis, G., Skaperas, S., Kapetanidou, I.A., Mamatas, L., Tsaoussidis, V.: Performance evaluation of kubernetes networking approaches across constraint edge environments (2024). https:\/\/arxiv.org\/abs\/2401.07674","DOI":"10.1109\/ISCC61673.2024.10733726"},{"key":"20_CR19","unstructured":"Kubernetes-based Event Driven Autoscaling: KEDA: Kubernetes-based event driven autoscaling. https:\/\/keda.sh\/ (2024). Accessed 31 Mar 2024"},{"key":"20_CR20","unstructured":"Kuchaiev, O., Ginsburg, B.: Factorization tricks for LSTM networks. arXiv preprint arXiv:1703.10722 (2017). https:\/\/arxiv.org\/abs\/1703.10722"},{"key":"20_CR21","doi-asserted-by":"publisher","DOI":"10.1145\/2931088.2931093","author":"S Lankes","year":"2016","unstructured":"Lankes, S.: Hermitcore: a unikernel for extreme scale computing. ACM (2016). https:\/\/doi.org\/10.1145\/2931088.2931093","journal-title":"ACM"},{"key":"20_CR22","doi-asserted-by":"publisher","DOI":"10.1080\/17445760.2019.1599889","author":"P Li","year":"2019","unstructured":"Li, P., Luo, B., Zhu, W., Xu, H.: Cluster-based distributed dynamic cuckoo filter system for Redis. Taylor & Francis (2019). https:\/\/doi.org\/10.1080\/17445760.2019.1599889","journal-title":"Taylor & Francis"},{"key":"20_CR23","doi-asserted-by":"publisher","unstructured":"Liu, P., et\u00a0al.: Scanflow-k8s: Agent-based framework for autonomic management and supervision of ml workflows in Kubernetes clusters. In: International Conference on Machine Learning and Applications (2020). https:\/\/doi.org\/10.1109\/ICMLA.2020.00041","DOI":"10.1109\/ICMLA.2020.00041"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Lolos, K., Konstantinou, I., Kantere, V., Koziris, N.: Elastic management of cloud applications using adaptive reinforcement learning. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 203\u2013212 (2017). https:\/\/api.semanticscholar.org\/CorpusID:19567764","DOI":"10.1109\/BigData.2017.8257928"},{"key":"20_CR25","doi-asserted-by":"publisher","DOI":"10.1504\/IJCC.2020.10034234","author":"M Malhotra","year":"2020","unstructured":"Malhotra, M., et al.: Dynamic scaling of web services for xen based virtual cloud environment. Int. J. Cloud Comput. (2020). https:\/\/doi.org\/10.1504\/IJCC.2020.10034234","journal-title":"Int. J. Cloud Comput."},{"issue":"10","key":"20_CR26","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"20_CR27","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316887","volume-title":"Markov Decision Processes: Discrete Stochastic Dynamic Programming","author":"ML Puterman","year":"1994","unstructured":"Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, NY, USA (1994)"},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Rao, J., Bu, X., Xu, C., Wang, L., Yin, G.: VCONF: a reinforcement learning approach to virtual machines auto-configuration. In: Proceedings of the 6th International Conference on Autonomic Computing (ICAC 2009), pp. 137\u2013146. ACM (2009)","DOI":"10.1145\/1555228.1555263"},{"key":"20_CR29","unstructured":"Sanfilippo, S., Stancliff, M.: Redis: A high-performance, in-memory, key-value store. Redis Labs (2013). https:\/\/redis.io\/"},{"key":"20_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2021.01.017","author":"AI Sanka","year":"2021","unstructured":"Sanka, A.I., Chowdhury, M., Cheung, R.: Efficient high-performance FPGA-REDIS hybrid NoSQL caching system for blockchain scalability. Elsevier (2021). https:\/\/doi.org\/10.1016\/j.comcom.2021.01.017","journal-title":"Elsevier"},{"key":"20_CR31","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)"},{"key":"20_CR32","doi-asserted-by":"publisher","DOI":"10.1145\/3341325.3341995","author":"B Thurgood","year":"2019","unstructured":"Thurgood, B., Lennon, R.G.: Cloud computing with Kubernetes cluster elastic scaling. ACM (2019). https:\/\/doi.org\/10.1145\/3341325.3341995","journal-title":"ACM"},{"issue":"1","key":"20_CR33","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1109\/TNSM.2021.3052837","volume":"18","author":"L Toka","year":"2021","unstructured":"Toka, L., Dobreff, G., Fodor, B., Sonkoly, B.: Machine learning-based scaling management for kubernetes edge clusters. IEEE Trans. Netw. Serv. Manage. 18(1), 958\u2013972 (2021). https:\/\/doi.org\/10.1109\/TNSM.2021.3052837","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Tsoumakos, D., Konstantinou, I., Boumpouka, C., Sioutas, S., Koziris, N.: Automated, elastic resource provisioning for NoSQL clusters using TIRAMOLA. In: 13th IEEE\/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 34\u201341. IEEE (2013)","DOI":"10.1109\/CCGrid.2013.45"},{"key":"20_CR35","unstructured":"Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., de\u00a0Freitas, N.: Dueling network architectures for Deep Reinforcement Learning. In: Proceedings of the 33rd International Conference on Machine Learning (ICML), pp. 1995\u20132003. JMLR.org (2016)"},{"issue":"3","key":"20_CR36","first-page":"279","volume":"8","author":"CJCH Watkins","year":"1992","unstructured":"Watkins, C.J.C.H., Dayan, P.: Q-learning. Mach. Learn. 8(3), 279\u2013292 (1992)","journal-title":"Mach. Learn."},{"issue":"16","key":"20_CR37","doi-asserted-by":"publisher","first-page":"4621","DOI":"10.3390\/s20164621","volume":"20","author":"YJ Yeom","year":"2020","unstructured":"Yeom, Y.J., Kim, T., Park, D.H., Kim, S.: Horizontal pod autoscaling in Kubernetes for elastic container orchestration. Sensors 20(16), 4621 (2020)","journal-title":"Sensors"},{"key":"20_CR38","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2979650","author":"K Yu","year":"2020","unstructured":"Yu, K., Liu, Y., Wang, Q.: Review of deep reinforcement learning. IEEE Access (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2979650","journal-title":"IEEE Access"},{"key":"20_CR39","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01199","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Si, X., Hu, Z., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. (2019). https:\/\/doi.org\/10.1162\/neco_a_01199","journal-title":"Neural Comput."},{"key":"20_CR40","doi-asserted-by":"publisher","unstructured":"Zhu, H., Bai, Z., Li, J.: Harmonia: Near-linear scalability for replicated storage with in-network conflict detection. ACM (2019). https:\/\/doi.org\/10.14778\/3368289.3368301","DOI":"10.14778\/3368289.3368301"}],"container-title":["Lecture Notes in Computer Science","Database and Expert Systems Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-02049-9_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T09:46:37Z","timestamp":1757411197000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-02049-9_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,18]]},"ISBN":["9783032020482","9783032020499"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-02049-9_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,18]]},"assertion":[{"value":"18 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests relevant to this work.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bangkok","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"36","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dexa.org\/2025\/dexa2025.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}