{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T16:51:42Z","timestamp":1773075102513,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031209833","type":"print"},{"value":"9783031209840","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-20984-0_32","type":"book-chapter","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T01:02:58Z","timestamp":1669078978000},"page":"449-464","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Cost-Aware Dynamic Multi-Workflow Scheduling in\u00a0Cloud Data Center Using Evolutionary Reinforcement Learning"],"prefix":"10.1007","author":[{"given":"Victoria","family":"Huang","sequence":"first","affiliation":[]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Kameron","family":"Christopher","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.jpdc.2015.10.001","volume":"87","author":"SG Ahmad","year":"2016","unstructured":"Ahmad, S.G., Liew, C.S., Munir, E.U., Ang, T.F., Khan, S.U.: A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems. J. Parallel Distrib. Comput. 87, 80\u201390 (2016)","journal-title":"J. Parallel Distrib. Comput."},{"key":"32_CR2","doi-asserted-by":"publisher","first-page":"134783","DOI":"10.1109\/ACCESS.2021.3116716","volume":"9","author":"R Alsurdeh","year":"2021","unstructured":"Alsurdeh, R., Calheiros, R.N., Matawie, K.M., Javadi, B.: Hybrid workflow scheduling on edge cloud computing systems. IEEE Access 9, 134783\u2013134799 (2021)","journal-title":"IEEE Access"},{"issue":"1","key":"32_CR3","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/TPDS.2018.2849396","volume":"30","author":"V Arabnejad","year":"2019","unstructured":"Arabnejad, V., Bubendorfer, K., Ng, B.: Budget and deadline aware e-science workflow scheduling in clouds. IEEE Trans. Parallel Distrib. Syst. 30(1), 29\u201344 (2019)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-Scale Science, pp. 1\u201310 (2008)","DOI":"10.1109\/WORKS.2008.4723958"},{"issue":"6","key":"32_CR5","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1016\/j.jpdc.2011.01.008","volume":"71","author":"EK Byun","year":"2011","unstructured":"Byun, E.K., Kee, Y.S., Kim, J.S., Deelman, E., Maeng, S.: BTS: resource capacity estimate for time-targeted science workflows. J. Parallel Distrib. Comput. 71(6), 848\u2013862 (2011)","journal-title":"J. Parallel Distrib. Comput."},{"issue":"4","key":"32_CR6","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1109\/TSC.2018.2866421","volume":"14","author":"H Chen","year":"2018","unstructured":"Chen, H., Zhu, X., Liu, G., Pedrycz, W.: Uncertainty-aware online scheduling for real-time workflows in cloud service environment. IEEE Trans. Serv. Comput. 14(4), 1167\u20131178 (2018)","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"12","key":"32_CR7","doi-asserted-by":"publisher","first-page":"10823","DOI":"10.1007\/s12652-020-02884-1","volume":"12","author":"T Dong","year":"2021","unstructured":"Dong, T., Xue, F., Xiao, C., Zhang, J.: Workflow scheduling based on deep reinforcement learning in the cloud environment. J. Ambient. Intell. Humaniz. Comput. 12(12), 10823\u201310835 (2021). https:\/\/doi.org\/10.1007\/s12652-020-02884-1","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"32_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1007\/978-3-030-59051-2_6","volume-title":"Database and Expert Systems Applications","author":"K-R Escott","year":"2020","unstructured":"Escott, K.-R., Ma, H., Chen, G.: Genetic programming based hyper heuristic approach for dynamic workflow scheduling in the cloud. In: Hartmann, S., K\u00fcng, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DEXA 2020. LNCS, vol. 12392, pp. 76\u201390. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59051-2_6"},{"issue":"6","key":"32_CR9","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1109\/TPDS.2019.2961098","volume":"31","author":"HR Faragardi","year":"2020","unstructured":"Faragardi, H.R., Saleh Sedghpour, M.R., Fazliahmadi, S., Fahringer, T., Rasouli, N.: GRP-HEFT: a budget-constrained resource provisioning scheme for workflow scheduling in IaaS clouds. IEEE Trans. Parallel Distrib. Syst. 31(6), 1239\u20131254 (2020)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Genez, T.A.L., Bittencourt, L.F., Madeira, E.R.M.: Workflow scheduling for SaaS\/PaaS cloud providers considering two SLA levels. In: 2012 IEEE Network Operations and Management Symposium, pp. 906\u2013912 (2012)","DOI":"10.1109\/NOMS.2012.6212007"},{"issue":"4","key":"32_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3418501","volume":"21","author":"F Hoseiny","year":"2021","unstructured":"Hoseiny, F., Azizi, S., Shojafar, M., Tafazolli, R.: Joint QoS-aware and cost-efficient task scheduling for fog-cloud resources in a volunteer computing system. ACM Trans. Internet Technol. (TOIT) 21(4), 1\u201321 (2021)","journal-title":"ACM Trans. Internet Technol. (TOIT)"},{"issue":"3","key":"32_CR12","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1016\/j.future.2012.08.015","volume":"29","author":"G Juve","year":"2013","unstructured":"Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Futur. Gener. Comput. Syst. 29(3), 682\u2013692 (2013)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"2","key":"32_CR13","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1007\/s10586-021-03454-6","volume":"25","author":"H Li","year":"2022","unstructured":"Li, H., Huang, J., Wang, B., Fan, Y.: Weighted double deep q-network based reinforcement learning for bi-objective multi-workflow scheduling in the cloud. Clust. Comput. 25(2), 751\u2013768 (2022)","journal-title":"Clust. Comput."},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Liessner, R., Schmitt, J., Dietermann, A., B\u00e4ker, B.: Hyperparameter optimization for deep reinforcement learning in vehicle energy management. In: ICAART (2), pp. 134\u2013144 (2019)","DOI":"10.5220\/0007364701340144"},{"issue":"3","key":"32_CR15","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1109\/TCC.2019.2906300","volume":"9","author":"J Liu","year":"2019","unstructured":"Liu, J., et al.: Online multi-workflow scheduling under uncertain task execution time in IaaS clouds. IEEE Trans. Cloud Comput. 9(3), 1180\u20131194 (2019)","journal-title":"IEEE Trans. Cloud Comput."},{"key":"32_CR16","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.eswa.2016.02.034","volume":"55","author":"P Lopez-Garcia","year":"2016","unstructured":"Lopez-Garcia, P., Onieva, E., Osaba, E., Masegosa, A.D., Perallos, A.: GACE: a meta-heuristic based in the hybridization of genetic algorithms and cross entropy methods for continuous optimization. Expert Syst. Appl. 55, 508\u2013519 (2016)","journal-title":"Expert Syst. Appl."},{"key":"32_CR17","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.jnca.2016.01.018","volume":"66","author":"M Masdari","year":"2016","unstructured":"Masdari, M., ValiKardan, S., Shahi, Z., Azar, S.I.: Towards workflow scheduling in cloud computing: a comprehensive analysis. J. Netw. Comput. Appl. 66, 64\u201382 (2016)","journal-title":"J. Netw. Comput. Appl."},{"issue":"4","key":"32_CR18","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/MCSE.2019.2906593","volume":"21","author":"H Oliver","year":"2019","unstructured":"Oliver, H., et al.: Workflow automation for cycling systems. Comput. Sci. Eng. 21(4), 7\u201321 (2019)","journal-title":"Comput. Sci. Eng."},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Pandey, S., Wu, L., Guru, S.M., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 400\u2013407 (2010)","DOI":"10.1109\/AINA.2010.31"},{"issue":"1","key":"32_CR20","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s11227-019-03033-y","volume":"76","author":"Y Qin","year":"2020","unstructured":"Qin, Y., Wang, H., Yi, S., Li, X., Zhai, L.: An energy-aware scheduling algorithm for budget-constrained scientific workflows based on multi-objective reinforcement learning. J. Supercomput. 76(1), 455\u2013480 (2020)","journal-title":"J. Supercomput."},{"issue":"2","key":"32_CR21","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1109\/TCC.2014.2314655","volume":"2","author":"MA Rodriguez","year":"2014","unstructured":"Rodriguez, M.A., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222\u2013235 (2014)","journal-title":"IEEE Trans. Cloud Comput."},{"key":"32_CR22","unstructured":"Salimans, T., Ho, J., Chen, X., Sidor, S., Sutskever, I.: Evolution strategies as a scalable alternative to reinforcement learning. arXiv preprint arXiv:1703.03864 (2017)"},{"key":"32_CR23","unstructured":"Schulman, J., Levine, S., Abbeel, P., Jordan, M., Moritz, P.: Trust region policy optimization. In: International Conference on Machine Learning (ICML), pp. 1889\u20131897 (2015)"},{"key":"32_CR24","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Suresh Kumar, D., Jagadeesh Kannan, R.: Reinforcement learning-based controller for adaptive workflow scheduling in multi-tenant cloud computing. Int. J. Electr. Eng. Educ. 0020720919894199 (2020)","DOI":"10.1177\/0020720919894199"},{"issue":"3","key":"32_CR26","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1109\/71.993206","volume":"13","author":"H Topcuoglu","year":"2002","unstructured":"Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260\u2013274 (2002)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"32_CR27","doi-asserted-by":"publisher","first-page":"39974","DOI":"10.1109\/ACCESS.2019.2902846","volume":"7","author":"Y Wang","year":"2019","unstructured":"Wang, Y., et al.: Multi-objective workflow scheduling with deep-q-network-based multi-agent reinforcement learning. IEEE Access 7, 39974\u201339982 (2019)","journal-title":"IEEE Access"},{"issue":"3","key":"32_CR28","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1109\/TSC.2013.49","volume":"7","author":"L Wu","year":"2014","unstructured":"Wu, L., Garg, S.K., Versteeg, S., Buyya, R.: SLA-based resource provisioning for hosted software-as-a-service applications in cloud computing environments. IEEE Trans. Serv. Comput. 7(3), 465\u2013485 (2014)","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"12","key":"32_CR29","doi-asserted-by":"publisher","first-page":"3401","DOI":"10.1109\/TPDS.2017.2735400","volume":"28","author":"Q Wu","year":"2017","unstructured":"Wu, Q., Ishikawa, F., Zhu, Q., Xia, Y., Wen, J.: Deadline-constrained cost optimization approaches for workflow scheduling in clouds. IEEE Trans. Parallel Distrib. Syst. 28(12), 3401\u20133412 (2017)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"32_CR30","doi-asserted-by":"crossref","unstructured":"Xiaoyong, Y., Ying, L., Tong, J., Tiancheng, L., Zhonghai, W.: An analysis on availability commitment and penalty in cloud SLA. In: 2015 IEEE 39th Annual Computer Software and Applications Conference, vol. 2, pp. 914\u2013919 (2015)","DOI":"10.1109\/COMPSAC.2015.39"},{"key":"32_CR31","doi-asserted-by":"crossref","unstructured":"Yang, Y., Chen, G., Ma, H., Zhang, M., Huang, V.: Budget and SLA aware dynamic workflow scheduling in cloud computing with heterogeneous resources. In: 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 2141\u20132148. IEEE (2021)","DOI":"10.1109\/CEC45853.2021.9504709"},{"key":"32_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-5071-8","volume-title":"Cloud Broker and Cloudlet for Workflow Scheduling","author":"CH Youn","year":"2017","unstructured":"Youn, C.H., Chen, M., Dazzi, P.: Cloud Broker and Cloudlet for Workflow Scheduling. Springer, Singapore (2017). https:\/\/doi.org\/10.1007\/978-981-10-5071-8"}],"container-title":["Lecture Notes in Computer Science","Service-Oriented Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20984-0_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T20:04:41Z","timestamp":1734984281000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20984-0_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031209833","9783031209840"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20984-0_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSOC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Service-Oriented Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seville","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icsoc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icsoc2022.spilab.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}