{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:14:10Z","timestamp":1774023250562,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031479687","type":"print"},{"value":"9783031479694","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-47969-4_17","type":"book-chapter","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T20:02:06Z","timestamp":1701374526000},"page":"210-223","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Efficient Resource Provisioning in Critical Infrastructures Based on Multi-Agent Rollout Enabled by Deep Q-Learning"],"prefix":"10.1007","author":[{"given":"Polyzois","family":"Soumplis","sequence":"first","affiliation":[]},{"given":"Panagiotis","family":"Kokkinos","sequence":"additional","affiliation":[]},{"given":"Emmanouel","family":"Varvarigos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Chen, T.M.: Smart grids, smart cities need better networks. IEEE Netw. 24(2), 2\u20133 (2010)","DOI":"10.1109\/MNET.2010.5430136"},{"issue":"5","key":"17_CR2","doi-asserted-by":"publisher","first-page":"4637","DOI":"10.1109\/TSG.2017.2665646","volume":"9","author":"JV Milanovi\u0107","year":"2018","unstructured":"Milanovi\u0107, J.V., Zhu, W.: Modeling of interconnected critical infrastructure systems using complex network theory. IEEE Trans. Smart Grid 9(5), 4637\u20134648 (2018)","journal-title":"IEEE Trans. Smart Grid"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Varga, P., et al.: Making system of systems interoperable \u2013 the core components of the arrowhead framework. J. Netw. Comput. Appl. 81, 85\u201395 (2017). ISSN 1084-8045","DOI":"10.1016\/j.jnca.2016.08.028"},{"key":"17_CR4","unstructured":"Bertsekas, D.P.: Multiagent rollout algorithms and reinforcement learning. CoRR abs\/1910.00120 (2019)"},{"key":"17_CR5","doi-asserted-by":"publisher","first-page":"77880","DOI":"10.1109\/ACCESS.2018.2884251","volume":"6","author":"X Li","year":"2018","unstructured":"Li, X., Lian, Z., Qin, X., Jie, W.: Topology-aware resource allocation for IoT services in clouds. IEEE Access 6, 77880\u201377889 (2018)","journal-title":"IEEE Access"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Santoro, D., Zozin, D., Pizzolli, D., De Pellegrini, F., Cretti, S.: Foggy: a platform for workload orchestration in a fog computing environment. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) (2017)","DOI":"10.1109\/CloudCom.2017.62"},{"issue":"3","key":"17_CR7","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1016\/j.jnca.2012.01.006","volume":"35","author":"BA Zubair","year":"2012","unstructured":"Zubair, B.A.: Multi-agent systems for protecting critical infrastructures: a survey. J. Netw. Comput. Appl. 35(3), 1151\u20131161 (2012)","journal-title":"J. Netw. Comput. Appl."},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Pipattanasomporn, M., Feroze, H., Rahman, S.: Multi-agent systems in a distributed smart grid: design and implementation. In: 2009 IEEE\/PES Power Systems Conference and Exposition, Seattle, WA, USA, pp. 1\u20138 (2009)","DOI":"10.1109\/PSCE.2009.4840087"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Panfili, M., Giuseppi, A., Fiaschetti, A., Al-Jibreen, H.B., Pietrabissa, A., Priscoli, F.D.: A game-theoretical approach to cyber-security of critical infrastructures based on multi-agent reinforcement learning. In: 2018 26th Mediterranean Conference on Control and Automation (MED), Zadar, Croatia, pp. 460\u2013465 (2018)","DOI":"10.1109\/MED.2018.8442695"},{"key":"17_CR10","doi-asserted-by":"publisher","first-page":"6923","DOI":"10.3390\/s21206923","volume":"21","author":"AA Mutlag","year":"2021","unstructured":"Mutlag, A.A., et al.: Multi-agent systems in fog-cloud computing for critical healthcare task management model (CHTM) used for ECG monitoring. Sensors 21, 6923 (2021)","journal-title":"Sensors"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Alfakih, T., Hassan, M.M., Gumaei, A., Savaglio, C., Fortino, G.: Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA. IEEE Access 8, 54074\u201354084 (2020)","DOI":"10.1109\/ACCESS.2020.2981434"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, W., Deng, H., Li, X.: Efficient resource allocation for security-aware task offloading in MEC system using DVS. Electronics 11(19), 3032 (2022)","DOI":"10.3390\/electronics11193032"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Chen, L., et al.: IoT microservice deployment in edge-cloud hybrid environment using reinforcement learning. In: IEEE Internet of Things Journal, vol. 8, no. 16, pp. 12610\u201312622 (2021)","DOI":"10.1109\/JIOT.2020.3014970"},{"issue":"4","key":"17_CR14","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1109\/TNSM.2019.2947905","volume":"16","author":"PTA Quang","year":"2019","unstructured":"Quang, P.T.A., Hadjadj-Aoul, Y., Outtagarts, A.: A deep reinforcement learning approach for VNF forwarding graph embedding. IEEE Trans. Netw. Serv. Manage. 16(4), 1318\u20131331 (2019)","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"issue":"2","key":"17_CR15","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1109\/TCCN.2020.2988486","volume":"6","author":"M Bunyakitanon","year":"2020","unstructured":"Bunyakitanon, M., Vasilakos, X., Nejabati, R., Simeonidou, D.: End-to-end performance-based autonomous VNF placement with adopted reinforcement learning. IEEE Trans. Cogn. Commun. Netw. 6(2), 534\u2013547 (2020)","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"issue":"2","key":"17_CR16","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/JSAC.2019.2959181","volume":"38","author":"J Pei","year":"2020","unstructured":"Pei, J., Hong, P., Pan, M., Liu, J., Zhou, J.: Optimal VNF placement via deep reinforcement learning in SDN\/NFV-enabled networks. IEEE J. Sel. Areas Commun. 38(2), 263\u2013278 (2020)","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"1","key":"17_CR17","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1109\/LCOMM.2020.3025298","volume":"25","author":"P Sun","year":"2021","unstructured":"Sun, P., Lan, J., Li, J., Guo, Z., Hu, Y.: Combining deep reinforcement learning with graph neural networks for optimal VNF placement. IEEE Commun. Lett. 25(1), 176\u2013180 (2021)","journal-title":"IEEE Commun. Lett."},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Jalodia, N., Henna, S., Davy, A.: Deep reinforcement learning for topology-aware VNF resource prediction in NFV environments. In: 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Dallas, TX, USA, pp. 1\u20135 (2019)","DOI":"10.1109\/NFV-SDN47374.2019.9040154"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Hester, T., et al.: Deep Q-learning from demonstrations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)","DOI":"10.1609\/aaai.v32i1.11757"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Pallewatta, S., Kostakos, V., Buyya, R.: Microservices-based IoT application placement within heterogeneous and resource constrained fog computing environments. In: 12th IEEE\/ACM International Conference on Utility and Cloud Computing, pp. 71\u201381 (2019)","DOI":"10.1145\/3344341.3368800"}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47969-4_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T20:03:37Z","timestamp":1701374617000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47969-4_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031479687","9783031479694"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47969-4_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lake Tahoe, NV","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isvc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.isvc.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"232% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43 (oral), 15 (poster),  25 (special tracks) out of 34 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}