{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T07:48:13Z","timestamp":1770536893271,"version":"3.49.0"},"publisher-location":"Cham","reference-count":25,"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_31","type":"book-chapter","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T01:02:58Z","timestamp":1669078978000},"page":"433-448","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Dual-Tree Genetic Programming for\u00a0Deadline-Constrained Dynamic Workflow Scheduling in\u00a0Cloud"],"prefix":"10.1007","author":[{"given":"Yifan","family":"Yang","sequence":"first","affiliation":[]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Mengjie","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.future.2019.04.029","volume":"100","author":"V Arabnejad","year":"2019","unstructured":"Arabnejad, V., Bubendorfer, K., Ng, B.: Dynamic multi-workflow scheduling: a deadline and cost-aware approach for commercial clouds. Futur. Gener. Comput. Syst. 100, 98\u2013108 (2019)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"31_CR2","unstructured":"Armbrust, M., et al.: Above the clouds: a Berkeley view of cloud computing. Technical report (2009)"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Chen, W., Deelman, E.: Workflowsim: a toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-Science, pp. 1\u20138. IEEE (2012)","DOI":"10.1109\/eScience.2012.6404430"},{"issue":"5","key":"31_CR4","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1109\/TPDS.2020.3041829","volume":"32","author":"H Djigal","year":"2020","unstructured":"Djigal, H., Feng, J., Lu, J., Ge, J.: IPPTS: an efficient algorithm for scientific workflow scheduling in heterogeneous computing systems. IEEE Trans. Parallel Distrib. Syst. 32(5), 1057\u20131071 (2020)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"31_CR5","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"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Escott, K.R., Ma, H., Chen, G.: A genetic programming hyper-heuristic approach to design high-level heuristics for dynamic workflow scheduling in cloud. In: 2020 IEEE Symposium Series on Computational Intelligence, pp. 3141\u20133148. IEEE (2020)","DOI":"10.1109\/SSCI47803.2020.9308261"},{"issue":"6","key":"31_CR7","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":"31_CR8","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.future.2019.08.012","volume":"102","author":"G Ismayilov","year":"2020","unstructured":"Ismayilov, G., Topcuoglu, H.R.: Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing. Futur. Gener. Comput. Syst. 102, 307\u2013322 (2020)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"4","key":"31_CR9","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1007\/s12597-021-00508-6","volume":"58","author":"A Rasouli Kenari","year":"2021","unstructured":"Rasouli Kenari, A., Shamsi, M.: A hyper-heuristic selector algorithm for cloud computing scheduling based on workflow features. Opsearch 58(4), 852\u2013868 (2021). https:\/\/doi.org\/10.1007\/s12597-021-00508-6","journal-title":"Opsearch"},{"issue":"3","key":"31_CR10","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."},{"issue":"2","key":"31_CR11","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1162\/evco_a_00256","volume":"28","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Mei, Y., Zhang, M., Zhang, Z.: A predictive-reactive approach with genetic programming and cooperative coevolution for the uncertain capacitated arc routing problem. Evol. Comput. 28(2), 289\u2013316 (2020)","journal-title":"Evol. Comput."},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"O\u2019Neill, M.: Riccardo Poli, William B. Langdon, Nicholas F. Mcphee: a field guide to genetic programming (2009)","DOI":"10.1007\/s10710-008-9073-y"},{"key":"31_CR13","doi-asserted-by":"publisher","unstructured":"Rizvi, N., Dharavath, R., Wang, L., Basava, A.: A workflow scheduling approach with modified fuzzy adaptive genetic algorithm in IaaS clouds. IEEE Trans. Serv. Comput. (2022). https:\/\/doi.org\/10.1109\/TSC.2022.3174112","DOI":"10.1109\/TSC.2022.3174112"},{"issue":"3","key":"31_CR14","doi-asserted-by":"publisher","first-page":"1500","DOI":"10.1109\/TCC.2020.3026338","volume":"10","author":"B Tan","year":"2022","unstructured":"Tan, B., Ma, H., Mei, Y., Zhang, M.: A cooperative coevolution genetic programming hyper-heuristics approach for on-line resource allocation in container-based clouds. IEEE Trans. Cloud Comput. 10(3), 1500\u20131514 (2022). https:\/\/doi.org\/10.1109\/TCC.2020.3026338","journal-title":"IEEE Trans. Cloud Comput."},{"issue":"3","key":"31_CR15","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":"31_CR16","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.future.2021.04.009","volume":"123","author":"L Versluis","year":"2021","unstructured":"Versluis, L., Iosup, A.: A survey of domains in workflow scheduling in computing infrastructures: community and keyword analysis, emerging trends, and taxonomies. Futur. Gener. Comput. Syst. 123, 156\u2013177 (2021)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"6","key":"31_CR17","doi-asserted-by":"publisher","first-page":"2715","DOI":"10.1109\/TCYB.2019.2933499","volume":"50","author":"ZJ Wang","year":"2020","unstructured":"Wang, Z.J., et al.: Dynamic group learning distributed particle swarm optimization for large-scale optimization and its application in cloud workflow scheduling. IEEE Trans. Cybern. 50(6), 2715\u20132729 (2020)","journal-title":"IEEE Trans. Cybern."},{"issue":"12","key":"31_CR18","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":"31_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/978-3-030-63833-7_4","volume-title":"Neural Information Processing","author":"J-P Xiao","year":"2020","unstructured":"Xiao, J.-P., Hu, X.-M., Chen, W.-N.: Dynamic cloud workflow scheduling with a heuristic-based encoding genetic algorithm. In: Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H., King, I. (eds.) ICONIP 2020. LNCS, vol. 12533, pp. 38\u201349. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-63833-7_4"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Xiao, Q.Z., Zhong, J., Feng, L., Luo, L., Lv, J.: A cooperative coevolution hyper-heuristic framework for workflow scheduling problem. IEEE Trans. Serv. Comput. 15(1), 150\u2013163 (2022)","DOI":"10.1109\/TSC.2019.2923912"},{"key":"31_CR21","doi-asserted-by":"publisher","unstructured":"Xie, Y., Gui, F.X., Wang, W.J., Chien, C.F.: A two-stage multi-population genetic algorithm with heuristics for workflow scheduling in heterogeneous distributed computing environments. IEEE Trans. Cloud Comput. (2021). https:\/\/doi.org\/10.1109\/TCC.2021.3137881","DOI":"10.1109\/TCC.2021.3137881"},{"key":"31_CR22","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, pp. 2141\u20132148 (2021)","DOI":"10.1109\/CEC45853.2021.9504709"},{"key":"31_CR23","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"},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Yu, Y., Feng, Y., Ma, H., Chen, A., Wang, C.: Achieving flexible scheduling of heterogeneous workflows in cloud through a genetic programming based approach. In: 2019 IEEE Congress on Evolutionary Computation, pp. 3102\u20133109. IEEE (2019)","DOI":"10.1109\/CEC.2019.8789896"},{"issue":"3","key":"31_CR25","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1109\/TEVC.2021.3056143","volume":"25","author":"F Zhang","year":"2021","unstructured":"Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Correlation coefficient-based recombinative guidance for genetic programming hyperheuristics in dynamic flexible job shop scheduling. IEEE Trans. Evol. Comput. 25(3), 552\u2013566 (2021)","journal-title":"IEEE Trans. Evol. Comput."}],"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_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T20:04:29Z","timestamp":1734984269000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20984-0_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031209833","9783031209840"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20984-0_31","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"}}]}}