{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:27:44Z","timestamp":1773030464355,"version":"3.50.1"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T00:00:00Z","timestamp":1587686400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T00:00:00Z","timestamp":1587686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1007\/s00500-020-04931-7","type":"journal-article","created":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T09:18:49Z","timestamp":1587719929000},"page":"16177-16199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Online scheduling of dependent tasks of cloud\u2019s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents"],"prefix":"10.1007","volume":"24","author":[{"given":"Ali","family":"Asghari","sequence":"first","affiliation":[]},{"given":"Mohammad Karim","family":"Sohrabi","sequence":"additional","affiliation":[]},{"given":"Farzin","family":"Yaghmaee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,24]]},"reference":[{"key":"4931_CR1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.simpat.2018.10.004","volume":"93","author":"F Abazari","year":"2019","unstructured":"Abazari F, Analoui M, Takabi H, Fu S (2019) MOWS: multi-objective workflow scheduling in cloud computing based on heuristic algorithm. Simul Model Pract Theory 93:119\u2013132","journal-title":"Simul Model Pract Theory"},{"key":"4931_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-030-10674-4","volume-title":"Feature selection and enhanced krill herd algorithm for text document clustering","author":"LMQ Abualigah","year":"2019","unstructured":"Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin, pp 1\u2013165"},{"issue":"1","key":"4931_CR3","first-page":"19","volume":"5","author":"LMQ Abualigah","year":"2015","unstructured":"Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19","journal-title":"Int J Comput Sci Eng Appl"},{"issue":"11","key":"4931_CR4","doi-asserted-by":"crossref","first-page":"4773","DOI":"10.1007\/s11227-017-2046-2","volume":"73","author":"LM Abualigah","year":"2017","unstructured":"Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773\u20134795","journal-title":"J Supercomput"},{"key":"4931_CR5","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.asoc.2017.06.059","volume":"60","author":"LM Abualigah","year":"2017","unstructured":"Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423\u2013435","journal-title":"Appl Soft Comput"},{"issue":"11","key":"4931_CR6","doi-asserted-by":"crossref","first-page":"4047","DOI":"10.1007\/s10489-018-1190-6","volume":"48","author":"LM Abualigah","year":"2018","unstructured":"Abualigah LM, Khader AT, Hanandeh ES (2018a) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047\u20134071","journal-title":"Appl Intell"},{"key":"4931_CR7","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.engappai.2018.05.003","volume":"73","author":"LM Abualigah","year":"2018","unstructured":"Abualigah LM, Khader AT, Hanandeh ES (2018b) A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111\u2013125","journal-title":"Eng Appl Artif Intell"},{"key":"4931_CR8","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.jocs.2017.07.018","volume":"25","author":"LM Abualigah","year":"2018","unstructured":"Abualigah LM, Khader AT, Hanandeh ES (2018c) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456\u2013466","journal-title":"J Comput Sci"},{"key":"4931_CR9","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.future.2015.01.007","volume":"50","author":"EN Alkhanak","year":"2015","unstructured":"Alkhanak EN, Lee SP, Khan SUR (2015) Cost-aware challenges for workflow scheduling approaches in cloud computing environments: taxonomy and opportunities. Future Gener Comput Syst 50:3\u201321","journal-title":"Future Gener Comput Syst"},{"key":"4931_CR10","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.future.2014.08.014","volume":"41","author":"J Altmann","year":"2014","unstructured":"Altmann J, Kashef MM (2014) Cost model based service placement in federated hybrid clouds. Future Gener Comput Syst 41:79\u201390","journal-title":"Future Gener Comput Syst"},{"issue":"12","key":"4931_CR11","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1007\/s00607-015-0455-8","volume":"97","author":"F Bahrpeyma","year":"2015","unstructured":"Bahrpeyma F, Haghighi H, Zakerolhosseini A (2015) An adaptive RL based approach for dynamic resource provisioning in cloud virtualized data centers. Computing 97(12):1209\u20131234","journal-title":"Computing"},{"key":"4931_CR12","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.procs.2015.07.384","volume":"57","author":"N Bansal","year":"2015","unstructured":"Bansal N, Maurya A, Kumar T, Singh M, Bansal S (2015) Cost performance of QoS Driven task scheduling in cloud computing. Procedia Comput Sci 57:126\u2013130","journal-title":"Procedia Comput Sci"},{"key":"4931_CR13","unstructured":"Barbierato E, Gribaudo M, Iacono M (2013) Modeling apache hive based applications in big data architectures. In: Proceedings of the 7th international conference on performance evaluation methodologies and tools. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp 30\u201338"},{"key":"4931_CR14","doi-asserted-by":"crossref","unstructured":"Barrett E, Howley E, Duggan J (2011) A learning architecture for scheduling workflow applications in the cloud. In: Ninth IEEE European conference on web services (ECOWS), 2011. IEEE, pp 83\u201390","DOI":"10.1109\/ECOWS.2011.27"},{"issue":"12","key":"4931_CR15","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1002\/cpe.2864","volume":"25","author":"E Barrett","year":"2013","unstructured":"Barrett E, Howley E, Duggan J (2013) Applying reinforcement learning towards automating resource allocation and application scalability in the cloud. Concurr Comput Pract Exp 25(12):1656\u20131674","journal-title":"Concurr Comput Pract Exp"},{"issue":"4","key":"4931_CR16","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1007\/s11036-018-0996-0","volume":"24","author":"JB Benifa","year":"2019","unstructured":"Benifa JB, Dejey D (2019) Rlpas: reinforcement learning-based proactive auto-scaler for resource provisioning in cloud environment. Mob Netw Appl 24(4):1348\u20131363","journal-title":"Mob Netw Appl"},{"key":"4931_CR17","doi-asserted-by":"crossref","unstructured":"Berral JL, Gavalda R, Torres J (2011) Adaptive scheduling on power-aware managed data-centers using machine learning. In: 12th IEEE\/ACM international conference on grid computing (GRID), 2011. IEEE, pp 66\u201373","DOI":"10.1109\/Grid.2011.18"},{"issue":"6","key":"4931_CR18","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.future.2008.12.001","volume":"25","author":"R Buyya","year":"2009","unstructured":"Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599\u2013616","journal-title":"Future Gener Comput Syst"},{"issue":"8","key":"4931_CR19","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1016\/j.future.2011.05.001","volume":"27","author":"EK Byun","year":"2011","unstructured":"Byun EK, Kee YS, Kim JS, Maeng S (2011) Cost optimized provisioning of elastic resources for application workflows. Future Gener Comput Syst 27(8):1011\u20131026","journal-title":"Future Gener Comput Syst"},{"issue":"3","key":"4931_CR20","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1109\/TCC.2017.2663426","volume":"7","author":"Z Cai","year":"2017","unstructured":"Cai Z, Li X, Ruiz R (2017) Resource provisioning for task-batch based workflows with deadlines in public clouds. IEEE Trans Cloud Comput 7(3):814\u2013826","journal-title":"IEEE Trans Cloud Comput"},{"issue":"1","key":"4931_CR21","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1002\/spe.995","volume":"41","author":"RN Calheiros","year":"2011","unstructured":"Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23\u201350","journal-title":"Softw Pract Exp"},{"key":"4931_CR22","first-page":"179","volume":"21","author":"G Cao","year":"2019","unstructured":"Cao G (2019) Topology-aware multi-objective virtual machine dynamic consolidation for cloud datacenter. Sustain Comput Inform Syst 21:179\u2013188","journal-title":"Sustain Comput Inform Syst"},{"key":"4931_CR23","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.future.2013.07.016","volume":"37","author":"A Castiglione","year":"2014","unstructured":"Castiglione A, Gribaudo M, Iacono M, Palmieri F (2014) Exploiting mean field analysis to model performances of big data architectures. Future Gener Comput Syst 37:203\u2013211","journal-title":"Future Gener Comput Syst"},{"issue":"2","key":"4931_CR24","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TSC.2011.7","volume":"5","author":"S Chaisiri","year":"2012","unstructured":"Chaisiri S, Lee BS, Niyato D (2012) Optimization of resource provisioning cost in cloud computing. IEEE Trans Serv Comput 5(2):164\u2013177","journal-title":"IEEE Trans Serv Comput"},{"key":"4931_CR25","doi-asserted-by":"crossref","unstructured":"Chen W, Deelman E (2012) Workflowsim: a toolkit for simulating scientific workflows in distributed environments. In: IEEE 8th international conference on E-science (e-science), 2012. IEEE, pp 1\u20138","DOI":"10.1109\/eScience.2012.6404430"},{"issue":"3","key":"4931_CR26","first-page":"279","volume":"8","author":"P Dayan","year":"1992","unstructured":"Dayan P, Watkins CJCH (1992) Q-learning. Mach Learn 8(3):279\u2013292","journal-title":"Mach Learn"},{"issue":"50","key":"4931_CR27","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.future.2015.02.001","volume":"1","author":"Y Ding","year":"2015","unstructured":"Ding Y, Qin X, Liu L, Wang T (2015) Energy efficient scheduling of virtual machines in cloud with deadline constraint. Future Gener Comput Syst. 1(50):62\u201374","journal-title":"Future Gener Comput Syst."},{"key":"4931_CR28","doi-asserted-by":"crossref","unstructured":"Duggan M, Flesk K, Duggan J, Howley E, Barrett E (2016) A reinforcement learning approach for dynamic selection of virtual machines in cloud data centres. In: 2016 sixth international conference on innovative computing technology (INTECH). IEEE, pp 92\u201397","DOI":"10.1109\/INTECH.2016.7845053"},{"key":"4931_CR29","doi-asserted-by":"crossref","unstructured":"Farahnakian F, Liljeberg P, Plosila J (2014) Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. In: 22nd Euromicro international conference on parallel, distributed and network-based processing (PDP), 2014. IEEE, pp 500\u2013507","DOI":"10.1109\/PDP.2014.109"},{"issue":"6","key":"4931_CR30","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1016\/j.jpdc.2010.04.004","volume":"71","author":"SK Garg","year":"2011","unstructured":"Garg SK, Yeo CS, Anandasivam A, Buyya R (2011) Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers. J Parallel Distrib Comput 71(6):732\u2013749","journal-title":"J Parallel Distrib Comput"},{"key":"4931_CR31","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.future.2017.02.022","volume":"78","author":"M Ghobaei-Arani","year":"2018","unstructured":"Ghobaei-Arani M, Jabbehdari S, Pourmina MA (2018) An autonomic resource provisioning approach for service-based cloud applications: a hybrid approach. Future Gener Comput Syst 78:191\u2013210","journal-title":"Future Gener Comput Syst"},{"issue":"1","key":"4931_CR32","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11227-014-1276-9","volume":"71","author":"S Hosseinimotlagh","year":"2015","unstructured":"Hosseinimotlagh S, Khunjush F, Samadzadeh R (2015) SEATS: smart energy-aware task scheduling in real-time cloud computing. J Supercomput 71(1):45\u201366","journal-title":"J Supercomput"},{"issue":"1","key":"4931_CR33","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.future.2011.05.027","volume":"28","author":"S Islam","year":"2012","unstructured":"Islam S, Keung J, Lee K, Liu A (2012) Empirical prediction models for adaptive resource provisioning in the cloud. Future Gener Comput Syst 28(1):155\u2013162","journal-title":"Future Gener Comput Syst"},{"key":"4931_CR34","unstructured":"https:\/\/confluence.pegasus.isi.edu\/display\/pegasus\/WorkflowGenerator"},{"key":"4931_CR36","volume-title":"The allocation of time and location information to activity-travel sequence data by means of reinforcement learning. In reinforcement learning","author":"W Janssens","year":"2008","unstructured":"Janssens W (2008) The allocation of time and location information to activity-travel sequence data by means of reinforcement learning. In reinforcement learning. InTech, London"},{"issue":"9\u201310","key":"4931_CR37","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s12243-019-00720-y","volume":"74","author":"Y Jin","year":"2019","unstructured":"Jin Y, Bouzid M, Kostadinov D, Aghasaryan A (2019) Resource management of cloud-enabled systems using model-free reinforcement learning. Ann Telecommun 74(9\u201310):625\u2013636","journal-title":"Ann Telecommun"},{"issue":"3","key":"4931_CR38","doi-asserted-by":"crossref","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 (2013) Characterizing and profiling scientific workflows. Future Gener Comput Syst 29(3):682\u2013692","journal-title":"Future Gener Comput Syst"},{"key":"4931_CR39","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1613\/jair.301","volume":"4","author":"LP Kaelbling","year":"1996","unstructured":"Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237\u2013285","journal-title":"J Artif Intell Res"},{"key":"4931_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jss.2016.07.006","volume":"124","author":"B Keshanchi","year":"2017","unstructured":"Keshanchi B, Souri A, Navimipour NJ (2017) An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J Syst Softw 124:1\u201321","journal-title":"J Syst Softw"},{"key":"4931_CR41","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.knosys.2015.02.012","volume":"80","author":"YC Lee","year":"2015","unstructured":"Lee YC, Han H, Zomaya AY, Yousif M (2015) Resource-efficient workflow scheduling in clouds. Knowl-Based Syst 80:153\u2013162","journal-title":"Knowl-Based Syst"},{"key":"4931_CR42","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1016\/j.future.2019.05.003","volume":"100","author":"C Li","year":"2019","unstructured":"Li C, Wang Y, Tang H, Luo Y (2019) Dynamic multi-objective optimized replica placement and migration strategies for SaaS applications in edge cloud. Future Gener Comput Syst 100:921\u2013937","journal-title":"Future Gener Comput Syst"},{"key":"4931_CR43","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jnca.2016.10.008","volume":"77","author":"M Liaqat","year":"2017","unstructured":"Liaqat M, Chang V, Gani A, Ab Hamid SH, Toseef M, Shoaib U, Ali RL (2017) Federated cloud resource management: review and discussion. J Netw Comput Appl 77:87\u2013105","journal-title":"J Netw Comput Appl"},{"issue":"4","key":"4931_CR44","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.1007\/s13369-018-3602-7","volume":"44","author":"SHH Madni","year":"2019","unstructured":"Madni SHH, Latiff MSA, Ali J (2019) Multi-objective-oriented cuckoo search optimization-based resource scheduling algorithm for clouds. Arab J Sci Eng 44(4):3585\u20133602","journal-title":"Arab J Sci Eng"},{"key":"4931_CR45","doi-asserted-by":"crossref","unstructured":"Maurer M, Breskovic I, Emeakaroha VC, Brandic I (2011) Revealing the MAPE loop for the autonomic management of cloud infrastructures. In: 2011 IEEE symposium on computers and communications (ISCC). IEEE, pp 147\u2013152","DOI":"10.1109\/ISCC.2011.5984008"},{"key":"4931_CR46","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.future.2014.07.002","volume":"41","author":"AS McGough","year":"2014","unstructured":"McGough AS, Forshaw M, Gerrard C, Wheater S, Allen B, Robinson P (2014) Comparison of a cost-effective virtual cloud cluster with an existing campus cluster. Future Gener Comput Syst 41:65\u201378","journal-title":"Future Gener Comput Syst"},{"key":"4931_CR47","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.jnca.2016.06.003","volume":"71","author":"AS Milani","year":"2016","unstructured":"Milani AS, Navimipour NJ (2016) Load balancing mechanisms and techniques in the cloud environments: systematic literature review and future trends. J Netw Comput Appl 71:86\u201398","journal-title":"J Netw Comput Appl"},{"key":"4931_CR48","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.jocs.2017.09.016","volume":"24","author":"MH Moghadam","year":"2018","unstructured":"Moghadam MH, Babamir SM (2018) Makespan reduction for dynamic workloads in cluster-based data grids using reinforcement-learning based scheduling. J Comput Sci 24:402\u2013412","journal-title":"J Comput Sci"},{"issue":"5","key":"4931_CR49","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1016\/j.jpdc.2014.01.004","volume":"74","author":"S Muppala","year":"2014","unstructured":"Muppala S, Chen G, Zhou X (2014) Multi-tier service differentiation by coordinated learning-based resource provisioning and admission control. J Parallel Distrib Comput 74(5):2351\u20132364","journal-title":"J Parallel Distrib Comput"},{"key":"4931_CR50","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/j.future.2018.11.049","volume":"94","author":"SMR Nouri","year":"2019","unstructured":"Nouri SMR, Li H, Venugopal S, Guo W, He M, Tian W (2019) Autonomic decentralized elasticity based on a reinforcement learning controller for cloud applications. Future Gener Comput Syst 94:765\u2013780","journal-title":"Future Gener Comput Syst"},{"key":"4931_CR51","first-page":"441","volume-title":"Reinforcement learning. Adaptation, learning, and optimization","author":"A Now\u00e9","year":"2012","unstructured":"Now\u00e9 A, Vrancx P, De Hauwere YM (2012) Game theory and multi-agent reinforcement learning. In: Wiering M, van Otterlo M (eds) Reinforcement learning. Adaptation, learning, and optimization, vol 12. Springer, Berlin, pp 441\u2013470"},{"key":"4931_CR52","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.jpdc.2017.05.001","volume":"117","author":"AI Orhean","year":"2018","unstructured":"Orhean AI, Pop F, Raicu I (2018) New scheduling approach using reinforcement learning for heterogeneous distributed systems. J Parallel Distrib Comput 117:292\u2013302","journal-title":"J Parallel Distrib Comput"},{"issue":"4","key":"4931_CR53","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1007\/s10586-015-0484-2","volume":"18","author":"Z Peng","year":"2015","unstructured":"Peng Z, Cui D, Zuo J, Li Q, Xu B, Lin W (2015) Random task scheduling scheme based on reinforcement learning in cloud computing. Clust Comput 18(4):1595\u20131607","journal-title":"Clust Comput"},{"issue":"6","key":"4931_CR54","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1016\/j.future.2012.01.009","volume":"29","author":"D Petcu","year":"2013","unstructured":"Petcu D, Macariu G, Panica S, Cr\u0103ciun C (2013) Portable cloud applications\u2014from theory to practice. Future Gener Comput Syst 29(6):1417\u20131430","journal-title":"Future Gener Comput Syst"},{"issue":"1","key":"4931_CR35","doi-asserted-by":"crossref","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 (2020) An energy-aware scheduling algorithm for budget-constrained scientific workflows based on multi-objective reinforcement learning. J Supercomput 76(1):455\u2013480","journal-title":"J Supercomput"},{"issue":"8","key":"4931_CR56","doi-asserted-by":"crossref","first-page":"e4949","DOI":"10.1002\/cpe.4949","volume":"31","author":"A Rehman","year":"2019","unstructured":"Rehman A, Hussain SS, ur Rehman Z, Zia S, Shamshirband S (2019) Multi-objective approach of energy efficient workflow scheduling in cloud environments. Concurr Comput Pract Exp 31(8):e4949","journal-title":"Concurr Comput Pract Exp"},{"key":"4931_CR57","doi-asserted-by":"crossref","unstructured":"Shin S, Kim Y, Lee S (2015) Deadline-guaranteed scheduling algorithm with improved resource utilization for cloud computing. In: Consumer communications and networking conference (CCNC), 2015 12th annual IEEE. IEEE, pp 814\u2013819","DOI":"10.1109\/CCNC.2015.7158082"},{"key":"4931_CR58","doi-asserted-by":"crossref","unstructured":"Simarro JLL, Moreno-Vozmediano R, Montero RS, Llorente IM (2011) Dynamic placement of virtual machines for cost optimization in multi-cloud environments. In: International conference on high performance computing and simulation (HPCS), 2011. IEEE, pp 1\u20137","DOI":"10.1109\/HPCSim.2011.5999800"},{"issue":"2","key":"4931_CR59","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10723-015-9359-2","volume":"14","author":"S Singh","year":"2016","unstructured":"Singh S, Chana I (2016) A survey on resource scheduling in cloud computing: issues and challenges. J Grid Comput 14(2):217\u2013264","journal-title":"J Grid Comput"},{"key":"4931_CR60","volume-title":"Reinforcement learning: an introduction","author":"RS Sutton","year":"1998","unstructured":"Sutton RS, Barto AG (1998) Reinforcement learning: an introduction, vol 1. MIT Press, Cambridge"},{"issue":"1","key":"4931_CR77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2200\/S00268ED1V01Y201005AIM009","volume":"4","author":"C Szepesv\u00e1ri","year":"2010","unstructured":"Szepesv\u00e1ri C (2010) Algorithms for reinforcement learning. Synth Lect Artif Intell Mach Learn 4(1):1\u2013103","journal-title":"Synth Lect Artif Intell Mach Learn"},{"key":"4931_CR61","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04118-8","author":"Z Tong","year":"2019","unstructured":"Tong Z, Deng X, Chen H, Mei J, Liu H (2019) QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04118-8","journal-title":"Neural Comput Appl"},{"key":"4931_CR62","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/j.future.2017.05.042","volume":"79","author":"AN Toosi","year":"2018","unstructured":"Toosi AN, Sinnott RO, Buyya R (2018) Resource provisioning for data-intensive applications with deadline constraints on hybrid clouds using Aneka. Future Gener Comput Syst 79:765\u2013775","journal-title":"Future Gener Comput Syst"},{"issue":"3","key":"4931_CR63","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/71.993206","volume":"13","author":"H Topcuoglu","year":"2002","unstructured":"Topcuoglu H, Hariri S, Wu MY (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260\u2013274","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"4","key":"4931_CR64","doi-asserted-by":"crossref","first-page":"8032","DOI":"10.1016\/j.eswa.2008.10.056","volume":"36","author":"M Vanhulsel","year":"2009","unstructured":"Vanhulsel M, Janssens D, Wets G, Vanhoof K (2009) Simulation of sequential data: an enhanced reinforcement learning approach. Expert Syst Appl 36(4):8032\u20138039","journal-title":"Expert Syst Appl"},{"key":"4931_CR65","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.future.2014.11.019","volume":"51","author":"MA Vasile","year":"2015","unstructured":"Vasile MA, Pop F, Tutueanu RI, Cristea V, Ko\u0142odziej J (2015) Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing. Future Gener Comput Syst 51:61\u201371","journal-title":"Future Gener Comput Syst"},{"key":"4931_CR66","doi-asserted-by":"crossref","unstructured":"Wang Q, Tan MM, Tang X, Cai W (2017) Minimizing cost in IaaS clouds via scheduled instance reservation. In: IEEE 37th international conference on distributed computing systems (ICDCS), 2017. IEEE, pp 1565\u20131574","DOI":"10.1109\/ICDCS.2017.16"},{"key":"4931_CR67","doi-asserted-by":"crossref","first-page":"39974","DOI":"10.1109\/ACCESS.2019.2902846","volume":"7","author":"Y Wang","year":"2019","unstructured":"Wang Y, Liu H, Zheng W, Xia Y, Li Y, Chen P, Guo K, Xie H (2019) Multi-objective workflow scheduling with deep-Q-network-based multi-agent reinforcement learning. IEEE Access 7:39974\u201339982","journal-title":"IEEE Access"},{"issue":"2","key":"4931_CR68","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1145\/2479957.2479960","volume":"43","author":"Z Wu","year":"2013","unstructured":"Wu Z, Madhyastha HV (2013) Understanding the latency benefits of multi-cloud webservice deployments. ACM SIGCOMM Comput Commun Rev 43(2):13\u201320","journal-title":"ACM SIGCOMM Comput Commun Rev"},{"issue":"5","key":"4931_CR69","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1109\/TPDS.2011.198","volume":"23","author":"Y Wu","year":"2012","unstructured":"Wu Y, Min G, Li K, Javadi B (2012) Modeling and analysis of communication networks in multicluster systems under spatio-temporal bursty traffic. IEEE Trans Parallel Distrib Syst 23(5):902\u2013912","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"1s","key":"4931_CR70","first-page":"29","volume":"13","author":"Y Wu","year":"2013","unstructured":"Wu Y, Min G, Zhu D, Yang LT (2013) An analytical model for on-chip interconnects in multimedia embedded systems. ACM Trans EmbedComput Syst 13(1s):29","journal-title":"ACM Trans EmbedComput Syst"},{"issue":"2","key":"4931_CR71","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.jpdc.2011.10.003","volume":"72","author":"CZ Xu","year":"2012","unstructured":"Xu CZ, Rao J, Bu X (2012) URL: a unified reinforcement learning approach for autonomic cloud management. J Parallel Distrib Comput 72(2):95\u2013105","journal-title":"J Parallel Distrib Comput"},{"issue":"3","key":"4931_CR72","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MNET.2019.1800386","volume":"33","author":"D Zeng","year":"2019","unstructured":"Zeng D, Gu L, Pan S, Cai J, Guo S (2019) Resource management at the network edge: a deep reinforcement learning approach. IEEE Netw 33(3):26\u201333","journal-title":"IEEE Netw"},{"issue":"2","key":"4931_CR73","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s10619-017-7215-z","volume":"36","author":"M Zhang","year":"2018","unstructured":"Zhang M, Li H, Liu L, Buyya R (2018) An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in Clouds. Distrib Parallel Databases 36(2):339\u2013368","journal-title":"Distrib Parallel Databases"},{"key":"4931_CR74","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/9136107","author":"W Zheng","year":"2016","unstructured":"Zheng W, Wang C, Zhang D (2016) A randomization approach for stochastic workflow scheduling in clouds. Sci Program. https:\/\/doi.org\/10.1155\/2016\/9136107","journal-title":"Sci Program"},{"key":"4931_CR75","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.future.2017.12.004","volume":"82","author":"W Zheng","year":"2018","unstructured":"Zheng W, Qin Y, Emmanuel B, Zhang D, Chen J (2018) Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds. Future Gener Comput Syst 82:244\u2013255","journal-title":"Future Gener Comput Syst"},{"key":"4931_CR76","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.procs.2019.06.018","volume":"154","author":"JH Zhong","year":"2019","unstructured":"Zhong JH, Peng ZP, Li QR, He JG (2019) Multi workflow fair scheduling scheme research based on reinforcement learning. Procedia Comput Sci 154:117\u2013123","journal-title":"Procedia Comput Sci"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-04931-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-020-04931-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-04931-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T05:37:24Z","timestamp":1619329044000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-020-04931-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,24]]},"references-count":76,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["4931"],"URL":"https:\/\/doi.org\/10.1007\/s00500-020-04931-7","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,24]]},"assertion":[{"value":"24 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}