{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:21:06Z","timestamp":1775913666601,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T00:00:00Z","timestamp":1701907200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T00:00:00Z","timestamp":1701907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"TUOHAI special project 2020 from Bohai Rim Energy Research Institute of Northeast Petroleum University","award":["HBHZX202002"],"award-info":[{"award-number":["HBHZX202002"]}]},{"name":"Project of Excellent and Middle-aged Scientific Research Innovation Team of Northeast Petroleum University","award":["KYCXTD201903"],"award-info":[{"award-number":["KYCXTD201903"]}]},{"name":"Heilongjiang Province Higher Education Teaching Reform Project","award":["SJGY20200125"],"award-info":[{"award-number":["SJGY20200125"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC330160204"],"award-info":[{"award-number":["2022YFC330160204"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The significant energy consumption within data centers is an essential contributor to global energy consumption and carbon emissions. Therefore, reducing energy consumption and carbon emissions in data centers plays a crucial role in sustainable development. Traditional cloud computing has reached a bottleneck, primarily due to high energy consumption. The emerging federated cloud approach can reduce the energy consumption and carbon emissions of cloud data centers by leveraging the geographical differences of multiple cloud data centers in a federated cloud. In this paper, we propose Eco-friendly Reinforcement Learning in Federated Cloud (ERLFC), a framework that uses reinforcement learning for task scheduling in a federated cloud environment. ERLFC aims to intelligently consider the state of each data center and effectively harness the variations in energy and carbon emission ratios across geographically distributed cloud data centers in the federated cloud. We build ERLFC using Actor-Critic algorithm, which select the appropriate data center to assign a task based on various factors such as energy consumption, cooling method, waiting time of the task, energy type, emission ratio, and total energy consumption of the current cloud data center and the details of the next task. To demonstrate the effectiveness of ERLFC, we conducted simulations based on real-world task execution data, and the results show that ERLFC can effectively reduce energy consumption and emissions during task execution. In comparison to Round Robin, Random, SO, and GJO algorithms, ERLFC achieves respective reductions of 1.09, 1.08, 1.21, and 1.26 times in terms of energy saving and emission reduction.<\/jats:p>","DOI":"10.1186\/s13677-023-00553-0","type":"journal-article","created":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T12:03:17Z","timestamp":1701950597000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Reinforcement learning based task scheduling for environmentally sustainable federated cloud computing"],"prefix":"10.1186","volume":"12","author":[{"given":"Zhibao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Shuaijun","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Lu","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Juntao","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Jinhua","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Raymond R.","family":"Bond","sequence":"additional","affiliation":[]},{"given":"Maurice D.","family":"Mulvenna","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,7]]},"reference":[{"key":"553_CR1","doi-asserted-by":"publisher","first-page":"74120","DOI":"10.1109\/ACCESS.2018.2883149","volume":"6","author":"A Taherkordi","year":"2018","unstructured":"Taherkordi A, Zahid F, Verginadis Y, Horn G (2018) Future cloud systems design: challenges and research directions. IEEE Access 6:74120\u201374150. https:\/\/doi.org\/10.1109\/ACCESS.2018.2883149","journal-title":"IEEE Access"},{"key":"553_CR2","doi-asserted-by":"crossref","unstructured":"Hazra D, Roy A, Midya S, et al (2018) Distributed task scheduling in cloud platform: a survey[C]\/\/Smart Computing and Informatics: Proceedings of the First International Conference on SCI 2016, Volume 1. Springer Singapore. 183\u2013191","DOI":"10.1007\/978-981-10-5544-7_19"},{"key":"553_CR3","doi-asserted-by":"publisher","first-page":"2005","DOI":"10.1109\/ICDCS.2019.00198","volume":"2019","author":"Y Fan","year":"2019","unstructured":"Fan Y, Tao L, Chen J (2019) Associated task scheduling based on dynamic finish time prediction for cloud computing. Proc - Int Conf Distrib Comput Syst 2019:2005\u20132014. https:\/\/doi.org\/10.1109\/ICDCS.2019.00198","journal-title":"Proc - Int Conf Distrib Comput Syst"},{"key":"553_CR4","doi-asserted-by":"crossref","unstructured":"Assis MRM, Bittencourt LF, Tolosana-Calasanz R, et al (2016) Cloud federations: requirements, properties, and architectures[M]\/\/Developing Interoperable and Federated Cloud Architecture. IGI Global. 1\u201341","DOI":"10.4018\/978-1-5225-0153-4.ch001"},{"issue":"1","key":"553_CR5","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/tst.2014.6733204","volume":"19","author":"Y Gu","year":"2014","unstructured":"Gu Y, Wang D, Liu C (2014) DR-Cloud: Multi-Cloud based disaster recovery service. Tsinghua Sci Technol 19(1):13\u201323. https:\/\/doi.org\/10.1109\/tst.2014.6733204","journal-title":"Tsinghua Sci Technol"},{"issue":"4","key":"553_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1147\/JRD.2009.5429058","volume":"53","author":"B Rochwerger","year":"2009","unstructured":"Rochwerger B, Breitgand D, Levy E, Galis A, Nagin K, Llorente IM, Montero R, Wolfsthal Y, Elmroth E, C\u00e1ceres J, Ben-Yehuda M, Emmerich W, Gal\u00e1n F (2009) The Reservoir model and architecture for open federated cloud computing. IBM J Res Dev 53(4):1\u201317. https:\/\/doi.org\/10.1147\/JRD.2009.5429058","journal-title":"IBM J Res Dev"},{"issue":"2","key":"553_CR7","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1109\/TASE.2020.3042409","volume":"19","author":"H Yuan","year":"2022","unstructured":"Yuan H, Bi J, Zhou MC (2022) Energy-efficient and QoS-optimized adaptive task scheduling and management in clouds. IEEE Trans Autom Sci Eng 19(2):1233\u20131244. https:\/\/doi.org\/10.1109\/TASE.2020.3042409","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"553_CR8","doi-asserted-by":"publisher","unstructured":"Luo L, Wu W, Di D, Zhang F, Yan Y, Mao Y (2012) A resource scheduling algorithm of cloud computing based on energy efficient optimization methods. 2012 Int Green Comput Conf IGCC 2012 (July 2007):0\u20135. https:\/\/doi.org\/10.1109\/IGCC.2012.6322251","DOI":"10.1109\/IGCC.2012.6322251"},{"issue":"6","key":"553_CR9","doi-asserted-by":"publisher","first-page":"1917","DOI":"10.1007\/s00500-017-2905-z","volume":"23","author":"V Dinesh Reddy","year":"2019","unstructured":"Dinesh Reddy V, Gangadharan GR, Rao GSVRK (2019) Energy-aware virtual machine allocation and selection in cloud data centers. Soft Comput 23(6):1917\u20131932. https:\/\/doi.org\/10.1007\/s00500-017-2905-z","journal-title":"Soft Comput"},{"issue":"10","key":"553_CR10","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/1400181.1400186","volume":"51","author":"P Kurp","year":"2008","unstructured":"Kurp P (2008) Green computing. Commun ACM 51(10):11\u201313. https:\/\/doi.org\/10.1145\/1400181.1400186","journal-title":"Commun ACM"},{"issue":"858","key":"553_CR11","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/EPTC.2008.4763421","volume":"2008","author":"D Wang","year":"2008","unstructured":"Wang D (2008) Meeting green computing challenges. 10th Electron Packag Technol Conf EPTC 2008(858):121\u2013126. https:\/\/doi.org\/10.1109\/EPTC.2008.4763421","journal-title":"10th Electron Packag Technol Conf EPTC"},{"issue":"October 2021","key":"553_CR12","doi-asserted-by":"publisher","first-page":"105959","DOI":"10.1016\/j.resconrec.2021.105959","volume":"176","author":"X Zhao","year":"2022","unstructured":"Zhao X, Ma X, Chen B, Shang Y, Song M (2022) Challenges toward carbon neutrality in China: strategies and countermeasures. Resour Conserv Recycl 176(October 2021):105959. https:\/\/doi.org\/10.1016\/j.resconrec.2021.105959","journal-title":"Resour Conserv Recycl"},{"issue":"2","key":"553_CR13","doi-asserted-by":"publisher","first-page":"3175","DOI":"10.32604\/cmc.2022.026041","volume":"72","author":"M Aldossary","year":"2022","unstructured":"Aldossary M, Alharbi HA (2022) An eco-friendly approach for reducing carbon emissions in cloud data centers. Comput Mater Contin 72(2):3175\u20133193. https:\/\/doi.org\/10.32604\/cmc.2022.026041","journal-title":"Comput Mater Contin"},{"key":"553_CR14","first-page":"1","volume":"2","author":"R Mata-Toledo","year":"2010","unstructured":"Mata-Toledo R, Gupta P (2010) Green data center: how green can we perform? J Technol Res 2:1\u20138","journal-title":"J Technol Res"},{"issue":"6","key":"553_CR15","first-page":"4","volume":"14","author":"W Forrest","year":"2008","unstructured":"Forrest W, Kaplan JM, Kindler N (2008) Data centers: how to cut carbon emissions and costs[J]. McKinsey Bus Technol 14(6):4\u201313","journal-title":"McKinsey Bus Technol"},{"issue":"1","key":"553_CR16","doi-asserted-by":"publisher","first-page":"117","DOI":"10.3390\/challe6010117","volume":"6","author":"A Andrae","year":"2015","unstructured":"Andrae A, Edler T (2015) On global electricity usage of communication technology: trends to 2030. Challenges 6(1):117\u2013157. https:\/\/doi.org\/10.3390\/challe6010117","journal-title":"Challenges"},{"key":"553_CR17","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.jnca.2016.06.014","volume":"72","author":"MRM Assis","year":"2016","unstructured":"Assis MRM, Bittencourt LF (2016) A survey on cloud federation architectures: identifying functional and non-functional properties. J Netw Comput Appl 72:51\u201371. https:\/\/doi.org\/10.1016\/j.jnca.2016.06.014","journal-title":"J Netw Comput Appl"},{"issue":"644048","key":"553_CR18","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-319-33313-7_25","volume":"567","author":"R Moreno-Vozmediano","year":"2016","unstructured":"Moreno-Vozmediano R, Huedo E, Llorente IM, Montero RS, Massonet P, Villari M, Merlino G, Celesti A, Levin A, Schour L, V\u00e1zquez C, Melis J, Spahr S, Whigham D (2016) BEACON: A cloud network federation framework. Commun Comput Inf Sci 567(644048):325\u2013337. https:\/\/doi.org\/10.1007\/978-3-319-33313-7_25","journal-title":"Commun Comput Inf Sci"},{"key":"553_CR19","doi-asserted-by":"crossref","unstructured":"Celesti A, Tusa F, Villari M (2012) Toward cloud federation: concepts and challenges[M]\/\/Achieving Federated and Self-Manageable Cloud Infrastructures: Theory and Practice. IGI Global. 1\u201317","DOI":"10.4018\/978-1-4666-1631-8.ch001"},{"issue":"1","key":"553_CR20","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1109\/COMST.2015.2481183","volume":"18","author":"M Dayarathna","year":"2016","unstructured":"Dayarathna M, Wen Y, Fan R (2016) Data center energy consumption modeling: a survey. IEEE Commun Surv Tutorials 18(1):732\u2013794. https:\/\/doi.org\/10.1109\/COMST.2015.2481183","journal-title":"IEEE Commun Surv Tutorials"},{"issue":"3","key":"553_CR21","first-page":"1259","volume":"27","author":"NM Nor","year":"2019","unstructured":"Nor NM, Hussin M, Abdullah R (2019) Energy-saving framework for data center from reduce, reuse and recycle perspectives. Pertanika J Sci Technol 27(3):1259\u20131277","journal-title":"Pertanika J Sci Technol"},{"issue":"3","key":"553_CR22","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/JSYST.2017.2700863","volume":"12","author":"J Wan","year":"2018","unstructured":"Wan J, Gui X, Zhang R, Fu L (2018) Joint cooling and server control in data centers: A cross-layer framework for holistic energy minimization. IEEE Syst J 12(3):24\u20132472. https:\/\/doi.org\/10.1109\/JSYST.2017.2700863","journal-title":"IEEE Syst J"},{"key":"553_CR23","doi-asserted-by":"publisher","unstructured":"Ohadi MM, Dessiatoun SV, Choo K, Pecht M, Lawler JV (2012) A comparison analysis of air, liquid, and two-phase cooling of data centers. Annu IEEE Semicond Therm Meas Manag Symp 58\u201363. https:\/\/doi.org\/10.1109\/STHERM.2012.6188826","DOI":"10.1109\/STHERM.2012.6188826"},{"issue":"August","key":"553_CR24","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1016\/j.apenergy.2017.08.037","volume":"205","author":"A Habibi Khalaj","year":"2017","unstructured":"Habibi Khalaj A, Halgamuge SK (2017) A Review on efficient thermal management of air- and liquid-cooled data centers: from chip to the cooling system. Appl Energy 205(August):1165\u20131188. https:\/\/doi.org\/10.1016\/j.apenergy.2017.08.037","journal-title":"Appl Energy"},{"key":"553_CR25","doi-asserted-by":"publisher","first-page":"131720","DOI":"10.1109\/ACCESS.2021.3114514","volume":"9","author":"M Aldossary","year":"2021","unstructured":"Aldossary M, Alharbi HA (2021) Towards a green approach for minimizing carbon emissions in fog-cloud architecture. IEEE Access 9:131720\u2013131732. https:\/\/doi.org\/10.1109\/ACCESS.2021.3114514","journal-title":"IEEE Access"},{"key":"553_CR26","doi-asserted-by":"crossref","unstructured":"Topcuoglu H, Hariri S, Society IC (2002) Performance-Effective and Low-Complexity. 13(3):260\u2013274","DOI":"10.1109\/71.993206"},{"issue":"4","key":"553_CR27","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TSUSC.2018.2822674","volume":"3","author":"N Hogade","year":"2018","unstructured":"Hogade N, Pasricha S, Siegel HJ, MacIejewski AA, Oxley MA, Jonardi E (2018) Minimizing energy costs for geographically distributed heterogeneous data centers. IEEE Trans Sustain Comput 3(4):318\u2013331. https:\/\/doi.org\/10.1109\/TSUSC.2018.2822674","journal-title":"IEEE Trans Sustain Comput"},{"key":"553_CR28","doi-asserted-by":"publisher","unstructured":"Ben Alla H, Ben Alla S, Touhafi A, Ezzati A (2018) Deadline and Energy Aware Task Scheduling in Cloud Computing. 2018 4th Int Conf Cloud Comput Technol Appl Cloudtech 2018. https:\/\/doi.org\/10.1109\/CloudTech.2018.8713338","DOI":"10.1109\/CloudTech.2018.8713338"},{"issue":"2","key":"553_CR29","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1109\/JSEE.2016.00047","volume":"27","author":"X Xu","year":"2016","unstructured":"Xu X, Cao L, Wang X (2016) Resource pre-allocation algorithms for low-energy task scheduling of cloud computing. J Syst Eng Electron 27(2):457\u2013469. https:\/\/doi.org\/10.1109\/JSEE.2016.00047","journal-title":"J Syst Eng Electron"},{"issue":"12","key":"553_CR30","doi-asserted-by":"publisher","first-page":"3426","DOI":"10.1109\/TPDS.2017.2730876","volume":"28","author":"G Xie","year":"2017","unstructured":"Xie G, Zeng G, Xiao X, Li R, Li K (2017) Energy-efficient scheduling algorithms for real-time parallel applications on heterogeneous distributed embedded systems. IEEE Trans Parallel Distrib Syst 28(12):3426\u20133442. https:\/\/doi.org\/10.1109\/TPDS.2017.2730876","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"July","key":"553_CR31","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.simpat.2018.07.006","volume":"87","author":"M Safari","year":"2018","unstructured":"Safari M, Khorsand R (2018) Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment. Simul Model Pract Theory 87(July):311\u2013326. https:\/\/doi.org\/10.1016\/j.simpat.2018.07.006","journal-title":"Simul Model Pract Theory"},{"issue":"1","key":"553_CR32","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s10723-015-9334-y","volume":"14","author":"Z Tang","year":"2016","unstructured":"Tang Z, Qi L, Cheng Z, Li K, Khan SU, Li K (2016) An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J Grid Comput 14(1):55\u201374. https:\/\/doi.org\/10.1007\/s10723-015-9334-y","journal-title":"J Grid Comput"},{"issue":"4","key":"553_CR33","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s10586-009-0119-6","volume":"13","author":"S Baskiyar","year":"2010","unstructured":"Baskiyar S, Abdel-Kader R (2010) Energy aware DAG scheduling on heterogeneous systems. Cluster Comput 13(4):373\u2013383. https:\/\/doi.org\/10.1007\/s10586-009-0119-6","journal-title":"Cluster Comput"},{"key":"553_CR34","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367. https:\/\/doi.org\/10.1016\/j.advengsoft.2016.01.008","journal-title":"Adv Eng Softw"},{"issue":"2019","key":"553_CR35","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1016\/j.asoc.2019.04.027","volume":"80","author":"H Peng","year":"2019","unstructured":"Peng H, Wen WS, Tseng ML, Li LL (2019) Joint optimization method for task scheduling time and energy consumption in mobile cloud computing environment. Appl Soft Comput J 80(2019):534\u2013545. https:\/\/doi.org\/10.1016\/j.asoc.2019.04.027","journal-title":"Appl Soft Comput J"},{"issue":"3","key":"553_CR36","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/s10586-012-0210-2","volume":"16","author":"Y Kessaci","year":"2013","unstructured":"Kessaci Y, Melab N, Talbi EG (2013) A Pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation. Cluster Comput 16(3):451\u2013468. https:\/\/doi.org\/10.1007\/s10586-012-0210-2","journal-title":"Cluster Comput"},{"issue":"3","key":"553_CR37","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1145\/1735971.1736048","volume":"45","author":"F Ahmad","year":"2010","unstructured":"Ahmad F, Vijaykumar TN (2010) Joint optimization of idle and cooling power in data centers while maintaining response time. ACM SIGPLAN Not 45(3):243\u2013256. https:\/\/doi.org\/10.1145\/1735971.1736048","journal-title":"ACM SIGPLAN Not"},{"key":"553_CR38","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1109\/ISCC.2017.8024631","volume":"0","author":"Y Wang","year":"2017","unstructured":"Wang Y, Zhang F, Wang R, Shi Y, Guo H, Liu Z (2017) Real-time task scheduling for joint energy efficiency optimization in data centers. Proc - IEEE Symp Comput Commun 0:838\u2013843. https:\/\/doi.org\/10.1109\/ISCC.2017.8024631","journal-title":"Proc - IEEE Symp Comput Commun"},{"key":"553_CR39","doi-asserted-by":"publisher","unstructured":"Ji K, Chi C, Marahatta A, Zhang F, Liu Z (2020) Energy Efficient Scheduling Based on Marginal Cost and Task Grouping in Data Centers. e-Energy 2020 - Proc 11th ACM Int Conf Futur Energy Syst 482\u2013488. https:\/\/doi.org\/10.1145\/3396851.3402657","DOI":"10.1145\/3396851.3402657"},{"issue":"3","key":"553_CR40","doi-asserted-by":"publisher","first-page":"3022","DOI":"10.1109\/JSYST.2019.2922436","volume":"13","author":"Q Jiang","year":"2019","unstructured":"Jiang Q, Leung VCM, Tang H, Xi HS (2019) Adaptive scheduling of stochastic task sequence for energy-efficient mobile cloud computing. IEEE Syst J 13(3):3022\u20133025. https:\/\/doi.org\/10.1109\/JSYST.2019.2922436","journal-title":"IEEE Syst J"},{"key":"553_CR41","doi-asserted-by":"publisher","unstructured":"Chase JS, Anderson DC, Thakar PN, Vahdat AM, Doyle RP (2001) Managing energy and server resources in hosting centers. 103. https:\/\/doi.org\/10.1145\/502043.502045","DOI":"10.1145\/502043.502045"},{"issue":"July","key":"553_CR42","doi-asserted-by":"publisher","first-page":"120972","DOI":"10.1016\/j.eswa.2023.120972","volume":"234","author":"J Zhang","year":"2023","unstructured":"Zhang J, Cheng L, Liu C, Zhao Z, Mao Y (2023) Cost-aware scheduling systems for real-time workflows in cloud: an approach based on Genetic Algorithm and Deep Reinforcement Learning. Expert Syst Appl 234(July):120972","journal-title":"Expert Syst Appl"},{"key":"553_CR43","doi-asserted-by":"publisher","unstructured":"Cheng L, Wang Y, Cheng F, Liu C, Zhao Z, Wang Y (2023) A Deep Reinforcement Learning-Based Preemptive Approach for Cost-Aware Cloud Job Scheduling. IEEE Trans Sustain Comput PP 1\u201312. https:\/\/doi.org\/10.1109\/TSUSC.2023.3303898","DOI":"10.1109\/TSUSC.2023.3303898"},{"issue":"21","key":"553_CR44","doi-asserted-by":"publisher","first-page":"18579","DOI":"10.1007\/s00521-022-07477-x","volume":"34","author":"L Cheng","year":"2022","unstructured":"Cheng L, Kalapgar A, Jain A, Wang Y, Qin Y, Li Y, Liu C (2022) Cost-aware real-time job scheduling for hybrid cloud using deep reinforcement learning. Neural Comput Appl 34(21):18579\u201318593. https:\/\/doi.org\/10.1007\/s00521-022-07477-x","journal-title":"Neural Comput Appl"},{"key":"553_CR45","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.future.2020.02.018","volume":"108","author":"D Ding","year":"2020","unstructured":"Ding D, Fan X, Zhao Y, Kang K, Yin Q, Zeng J (2020) Q-learning based dynamic task scheduling for energy-efficient cloud computing. Futur Gener Comput Syst 108:361\u2013371. https:\/\/doi.org\/10.1016\/j.future.2020.02.018","journal-title":"Futur Gener Comput Syst"},{"issue":"6","key":"553_CR46","doi-asserted-by":"publisher","first-page":"4171","DOI":"10.1007\/s10586-022-03630-2","volume":"25","author":"K Siddesha","year":"2022","unstructured":"Siddesha K, Jayaramaiah GV, Singh C (2022) A novel deep reinforcement learning scheme for task scheduling in cloud computing. Cluster Comput 25(6):4171\u20134188. https:\/\/doi.org\/10.1007\/s10586-022-03630-2","journal-title":"Cluster Comput"},{"key":"553_CR47","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1109\/PADSW.2018.8644639","volume":"2018","author":"L Yan","year":"2019","unstructured":"Yan L, Liu W, Bai D (2019) Temperature and power aware server placement optimization for enterprise data center. Proc Int Conf Parallel Distrib Syst - ICPADS 2018:433\u2013440. https:\/\/doi.org\/10.1109\/PADSW.2018.8644639","journal-title":"Proc Int Conf Parallel Distrib Syst - ICPADS"},{"key":"553_CR48","doi-asserted-by":"crossref","unstructured":"Kang KX, Ding D, Xie HM, Yin Q, Zeng J (2021) Adaptive drl-based task scheduling for energy-efficient cloud computing. IEEE Transactions on Network and Service Management","DOI":"10.1109\/TNSM.2021.3137926"},{"issue":"7920","key":"553_CR49","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1038\/d41586-022-01983-7","volume":"607","author":"E Gibney","year":"2022","unstructured":"Gibney E (2022) How to shrink AI\u2019s ballooning carbon footprint. Nature 607(7920):648\u2013648","journal-title":"Nature"},{"key":"553_CR50","unstructured":"Chen S (2023) \u2018RL_BasedTaskSchedulingForEnvironmentallSustatinableFCC\u2019, Available at: [https:\/\/github.com\/ShuaijunC\/RL_BasedTaskSchedulingForEnvironmentallSustatinableFCC\/tree\/master]. Accessed: 16\/11\/2023"},{"key":"553_CR51","first-page":"1","volume-title":"Proximal Policy Optimization Algorithms","author":"J Schulman","year":"2017","unstructured":"Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal Policy Optimization Algorithms. pp 1\u201312"},{"key":"553_CR52","unstructured":"Schulman J, Levine S, Abbeel P, et al (2015) Trust region policy optimization[C]\/\/International conference on machine learning. PMLR. 1889\u20131897"},{"key":"553_CR53","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/Cluster48925.2021.00031","volume":"2021","author":"N Grinsztajn","year":"2021","unstructured":"Grinsztajn N, Beaumont O, Jeannot E, Preux P (2021) READYS: a reinforcement learning based strategy for heterogeneous dynamic scheduling. Proc - IEEE Int Conf Clust Comput ICCC 2021:70\u201381. https:\/\/doi.org\/10.1109\/Cluster48925.2021.00031","journal-title":"Proc - IEEE Int Conf Clust Comput ICCC"},{"issue":"12","key":"553_CR54","doi-asserted-by":"publisher","first-page":"3135","DOI":"10.14778\/3476311.3476389","volume":"14","author":"T Akidau","year":"2021","unstructured":"Akidau T, Begoli E, Chernyak S, Hueske F, Knight K, Knowles K, Mills D, Sotolongo D (2021) Watermarks in stream processing systems: Semantics and comparative analysis of apache flink and google cloud dataflow. Proc VLDB Endow 14(12):3135\u20133147. https:\/\/doi.org\/10.14778\/3476311.3476389","journal-title":"Proc VLDB Endow"},{"key":"553_CR55","unstructured":"Charles Reiss, John Wilkes JH (2014) Google cluster-usage traces format schema 2014\u201311\u201317 external.pdf - Google Drive. Google Inc: 1\u201314 https:\/\/code.google.com\/apis\/storage\/"},{"issue":"1","key":"553_CR56","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1109\/TCC.2017.2732344","volume":"8","author":"TP Pham","year":"2020","unstructured":"Pham TP, Durillo JJ, Fahringer T (2020) Predicting workflow task execution time in the cloud using a two-stage machine learning approach. IEEE Trans Cloud Comput 8(1):256\u2013268. https:\/\/doi.org\/10.1109\/TCC.2017.2732344","journal-title":"IEEE Trans Cloud Comput"},{"key":"553_CR57","doi-asserted-by":"publisher","first-page":"108320","DOI":"10.1016\/j.knosys.2022.108320","volume":"242","author":"FA Hashim","year":"2022","unstructured":"Hashim FA, Hussien AG (2022) Snake optimizer: a novel meta-heuristic optimization algorithm. Knowledge-Based Syst 242:108320. https:\/\/doi.org\/10.1016\/j.knosys.2022.108320","journal-title":"Knowledge-Based Syst"},{"issue":"March","key":"553_CR58","doi-asserted-by":"publisher","first-page":"116924","DOI":"10.1016\/j.eswa.2022.116924","volume":"198","author":"N Chopra","year":"2022","unstructured":"Chopra N, Mohsin Ansari M (2022) Golden jackal optimization: a novel nature-inspired optimizer for engineering applications. Expert Syst Appl 198(March):116924. https:\/\/doi.org\/10.1016\/j.eswa.2022.116924","journal-title":"Expert Syst Appl"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-023-00553-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-023-00553-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-023-00553-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T12:05:34Z","timestamp":1701950734000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-023-00553-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,7]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["553"],"URL":"https:\/\/doi.org\/10.1186\/s13677-023-00553-0","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,7]]},"assertion":[{"value":"14 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"174"}}