{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T02:51:10Z","timestamp":1761965470366,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031530814"},{"type":"electronic","value":"9783031530821"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-53082-1_12","type":"book-chapter","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T18:02:52Z","timestamp":1706637772000},"page":"144-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Reinforcement Learning Based Intelligent Resource Allocation Techniques with Applications to Cloud Computing"],"prefix":"10.1007","author":[{"given":"Ramanpreet","family":"Kaur","sequence":"first","affiliation":[]},{"given":"Divya","family":"Anand","sequence":"additional","affiliation":[]},{"given":"Upinder","family":"Kaur","sequence":"additional","affiliation":[]},{"given":"Jaskiran","family":"Kaur","sequence":"additional","affiliation":[]},{"given":"Sahil","family":"Verma","sequence":"additional","affiliation":[]},{"family":"Kavita","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"12_CR1","first-page":"153432","volume":"4","author":"M Cheong","year":"2016","unstructured":"Cheong, M., Lee, H., Yeom, I., Woo, H.: SCARL: attentive reinforcement learning-based scheduling in a multi-resource heterogeneous cluster. IEEE Access 4, 153432\u2013153444 (2016)","journal-title":"IEEE Access"},{"key":"12_CR2","doi-asserted-by":"publisher","first-page":"14523","DOI":"10.1038\/s41598-022-18603-z","volume":"12","author":"NR Pradhan","year":"2022","unstructured":"Pradhan, N.R., Singh, A.P., Verma, S., et al.: A blockchain based lightweight peer-to-peer energy trading framework for secured high throughput micro-transactions. Sci. Rep. 12, 14523 (2022)","journal-title":"Sci. Rep."},{"key":"12_CR3","doi-asserted-by":"publisher","first-page":"17215","DOI":"10.1109\/ACCESS.2018.2814606","volume":"6","author":"H Liu","year":"2018","unstructured":"Liu, H., Liu, S., Zheng, K.: A reinforcement learning-based resource allocation scheme for cloud robotics. IEEE Access 6, 17215\u201317222 (2018)","journal-title":"IEEE Access"},{"issue":"8","key":"12_CR4","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.3390\/sym13081447","volume":"13","author":"G Ghosh","year":"2021","unstructured":"Ghosh, G., et al.: Secure surveillance systems using partial-regeneration-based non-dominated optimization and 5D-chaotic map. Symmetry 13(8), 1447 (2021). https:\/\/doi.org\/10.3390\/sym13081447","journal-title":"Symmetry"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Alsarhan, A., Itradat, A., Al-Dubai, A.Y., cZomaya, A.Y., Min, G.: Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments. IEEE Trans. Parallel Distrib. Syst. 29(1), 31\u201342 (2018). ISSN: 1045\u20139219","DOI":"10.1109\/TPDS.2017.2748578"},{"key":"12_CR6","doi-asserted-by":"publisher","first-page":"101227","DOI":"10.1016\/j.phycom.2020.101227","volume":"43","author":"X Tian","year":"2020","unstructured":"Tian, X., et al.: Power allocation scheme for maximizing spectral efficiency and energy efficiency tradeoff for uplink NOMA systems in B5G\/6G. Phys. Commun. 43, 101227 (2020)","journal-title":"Phys. Commun."},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Hu, J., Min, G., Zomaya, A.Y., El-Ghazawi, T.: Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning. IEEE Trans. Parallel Distrib. Syst. 31(4), 923\u2013934 (2019). ISSN: 1045-9219","DOI":"10.1109\/TPDS.2019.2953745"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Ghobaei-Arani, M., Jabbehdari, S., Pourmina, M.A.: An autonomic resources provisioning approach for service-based cloud applications: a hybrid approach. Future Gener. Comput. Syst. 78, 191\u2013210 (2017). ISSN: 0167-739X","DOI":"10.1016\/j.future.2017.02.022"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"He, B., Wang, J., Qi, Q, Sun, H., Liao, J.: Towards intelligent provisioning of virtualized network functions in cloud of things: a deep reinforcement learning based approach. IEEE Trans. Cloud Comput. 10(2), 1262\u20131274 (2020). ISSN: 2168-7161","DOI":"10.1109\/TCC.2020.2985651"},{"key":"12_CR10","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1007\/s11277-020-07398-9","volume":"25","author":"S Mostafavi","year":"2020","unstructured":"Mostafavi, S., Hakami, V.: A stochastic approximation approach for foresighted task scheduling in cloud computing. Wireless Pers. Commun. 25, 901\u2013925 (2020). https:\/\/doi.org\/10.1007\/s11277-020-07398-9","journal-title":"Wireless Pers. Commun."},{"key":"12_CR11","doi-asserted-by":"publisher","first-page":"e0250959","DOI":"10.1371\/journal.pone.0250959","volume":"16","author":"A Gandam","year":"2021","unstructured":"Gandam, A., et al.: An efficient post-processing adaptive filtering technique to rectifying the flickering effects. PLoS ONE 16, e0250959 (2021)","journal-title":"PLoS ONE"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Singh, D., Verma, S., Singla, J.: A neuro-fuzzy based medical intelligent system for the diagnosis of hepatitis B. In: Proceedings of the 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates, pp. 107\u2013111 (2021)","DOI":"10.1109\/ICCAKM50778.2021.9357765"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Thakkar, H., Dehury, C., Sahoo, P.: MUVINE: multi-stage virtual network embedding in cloud data centers using reinforcement learning based predictions. IEEE J. Sel. Areas Commun. 38(6), 1058\u20131074 (2020). ISSN: 0733-8716","DOI":"10.1109\/JSAC.2020.2986663"},{"key":"12_CR14","unstructured":"Ghosh, G., Kavita, Verma, S., Talib, M.N., Shah, M.H.: A systematic review on image encryption techniques. Turk. J. Comput. Math. Educ. 12, 3055\u20133059 (2021)"},{"issue":"6","key":"12_CR15","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/MCC.2018.1081063","volume":"4","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Yao, J., Guan, H.: Intelligent cloud resource management with deep reinforcement learning. IEEE Cloud Comput. 4(6), 60\u201369 (2017)","journal-title":"IEEE Cloud Comput."},{"key":"12_CR16","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-981-13-3393-4_17","volume-title":"Soft Computing and Signal Processing","author":"S Sharma","year":"2019","unstructured":"Sharma, S., Verma, S., Jyoti, K.: A new bat algorithm with distance computation capability and its applicability in routing for WSN. In: Wang, J., Reddy, G.R.M., Prasad, V.K., Reddy, V.S. (eds.) Soft Computing and Signal Processing. AISC, vol. 898, pp. 163\u2013171. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-3393-4_17"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Wei, X., Zhao, J., Zhou, L., Qian, Y.: Broad reinforcement learning for supporting fast autonomous IoT. IEEE Internet Things J. 7(8), 7010\u20137020 (2020). ISSN: 2327-4662","DOI":"10.1109\/JIOT.2020.2980198"},{"issue":"1","key":"12_CR18","first-page":"71","volume":"10","author":"P Kumar","year":"2021","unstructured":"Kumar, P., Verma, S.: Detection of wormhole attack in VANET. Natl. J. Syst. Inf. Technol. 10(1), 71 (2021)","journal-title":"Natl. J. Syst. Inf. Technol."},{"key":"12_CR19","doi-asserted-by":"publisher","first-page":"55112","DOI":"10.1109\/ACCESS.2018.2872674","volume":"6","author":"Y Wei","year":"2018","unstructured":"Wei, Y., Pan, L., Liu, S., Wu, L., Meng, X.: DRL-scheduling: an intelligent QoS-aware job scheduling framework for applications in clouds. IEEE Access 6, 55112\u201355125 (2018)","journal-title":"IEEE Access"},{"issue":"3","key":"12_CR20","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1002\/spe.2802","volume":"52","author":"A Kaur","year":"2020","unstructured":"Kaur, A., Singh, P., Batth, R., Lim, C.: Deep-Q learning-based heterogeneous earliest finish time scheduling algorithm for scientific workflows in cloud. Softw. Pract. Exp. 52(3), 689\u2013709 (2020)","journal-title":"Softw. Pract. Exp."},{"issue":"23","key":"12_CR21","doi-asserted-by":"publisher","first-page":"e5919","DOI":"10.1002\/cpe.5919","volume":"33","author":"G Rjoub","year":"2020","unstructured":"Rjoub, G., Bentahar, J., Wahab, O., Bataineh, A.: Deep and reinforcement learning for automated task scheduling in large-scale cloud computing systems. Concurr. Comput. Pract. Exp. 33(23), e5919 (2020)","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"12_CR22","doi-asserted-by":"publisher","first-page":"3319","DOI":"10.1109\/ACCESS.2019.2963051","volume":"8","author":"J Zhao","year":"2016","unstructured":"Zhao, J., Kong, M., Li, Q., Sun, X.: Contract-based computing resource management via deep reinforcement learning in vehicular fog computing. IEEE Access 8, 3319\u20133329 (2016)","journal-title":"IEEE Access"}],"container-title":["Communications in Computer and Information Science","Recent Trends in Image Processing and Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53082-1_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T18:08:32Z","timestamp":1706638112000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53082-1_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031530814","9783031530821"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53082-1_12","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RTIP2R","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Recent Trends in Image Processing and Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Derby","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"7 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rtip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rtip2r-conference.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT, Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"216","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":"62","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":"29% - 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":"2.39","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":"2.79","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}