{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T08:18:20Z","timestamp":1788250700105,"version":"build-2803163510"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U2468203"],"award-info":[{"award-number":["U2468203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08769-y","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:42:33Z","timestamp":1788244953000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MSQL: Task scheduling optimization via memory sensitive reinforcement learning in heterogeneous cloud environments"],"prefix":"10.1007","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6269-0471","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhong","family":"Hei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyun","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Tu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"8769_CR1","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.is.2014.07.006","volume":"47","author":"IAT Hashem","year":"2015","unstructured":"Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of big data on cloud computing: Review and open research issues. Inf Syst 47:98\u2013115","journal-title":"Inf Syst"},{"key":"8769_CR2","doi-asserted-by":"crossref","unstructured":"Rashid ZN, Zebari SR, Sharif KH, Jacksi K (2021) Distributed cloud computing and distributed parallel computing: A review. 2021 8th International Conference on Computer and Communication Engineering (ICCCE). IEEE, pp 167\u2013172","DOI":"10.1109\/ICOASE.2018.8548937"},{"key":"8769_CR3","doi-asserted-by":"publisher","first-page":"80813","DOI":"10.1109\/ACCESS.2019.2922196","volume":"7","author":"S Dong","year":"2019","unstructured":"Dong S, Abbas K, Jain R (2019) A survey on distributed denial of service (ddos) attacks in sdn and cloud computing environments. IEEE Access 7:80813\u201380828","journal-title":"IEEE Access"},{"key":"8769_CR4","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.aej.2024.12.070","volume":"118","author":"B Zhu","year":"2025","unstructured":"Zhu B, Niu L (2025) A privacy-preserving federated learning scheme with homomorphic encryption and edge computing. Alex Eng J 118:11\u201320","journal-title":"Alex Eng J"},{"key":"8769_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2022.132365","volume":"363","author":"H Yuan","year":"2022","unstructured":"Yuan H, Feng K, Li W, Sun X (2022) Multi-objective optimization of virtual energy hub plant integrated with data center and plug-in electric vehicles under a mixed robust-stochastic model. J Clean Prod 363:132365","journal-title":"J Clean Prod"},{"key":"8769_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2026.104655","volume":"74","author":"G Yu","year":"2026","unstructured":"Yu G, Wang Y, Wang Z, Chen L, Zheng Y, Liu Y (2026) Llms in industrial domains: A systematic review of adaptation techniques and applications from the product lifecycle perspective. Adv Eng Inform 74:104655","journal-title":"Adv Eng Inform"},{"key":"8769_CR7","doi-asserted-by":"publisher","first-page":"54773","DOI":"10.1109\/ACCESS.2023.3281839","volume":"11","author":"X Gao","year":"2023","unstructured":"Gao X (2023) Tas: A temperature-aware scheduling for heterogeneous computing. IEEE Access 11:54773\u201354781","journal-title":"IEEE Access"},{"issue":"12","key":"8769_CR8","doi-asserted-by":"publisher","first-page":"4123","DOI":"10.1002\/cpe.4123","volume":"29","author":"M Xu","year":"2017","unstructured":"Xu M, Tian W, Buyya R (2017) A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurrency Comput Practice Experience 29(12):4123","journal-title":"Concurrency Comput Practice Experience"},{"issue":"9","key":"8769_CR9","first-page":"35","volume":"4","author":"C Perera","year":"2024","unstructured":"Perera C (2024) Optimizing performance in parallel and distributed computing systems for large-scale applications. J Adv Comput Syst 4(9):35\u201344","journal-title":"J Adv Comput Syst"},{"key":"8769_CR10","doi-asserted-by":"crossref","unstructured":"Kannan S, Gavrilovska A, Gupta V, Schwan K (2017) Heteroos: Os design for heterogeneous memory management in datacenter. Proceedings of the 44th Annual International Symposium on Computer Architecture. ACM, pp 521\u2013534","DOI":"10.1145\/3079856.3080245"},{"issue":"8","key":"8769_CR11","first-page":"1","volume":"57","author":"G Liu","year":"2025","unstructured":"Liu G, Lin W, Zhang H, Lin J, Peng S, Li K (2025) Public datasets for cloud computing: A comprehensive survey. ACM Comput Surv 57(8):1\u201338","journal-title":"ACM Comput Surv"},{"issue":"10","key":"8769_CR12","doi-asserted-by":"publisher","first-page":"20761","DOI":"10.1109\/JIOT.2026.3667862","volume":"13","author":"H Luo","year":"2026","unstructured":"Luo H, Feng Q, Sun W, Li W, Li Q, Zhao W, Liu Z (2026) Fd-mamba with neural observer and frequency-enhanced update for incipient feeder fault detection. IEEE Internet Things J 13(10):20761\u201320772","journal-title":"IEEE Internet Things J"},{"key":"8769_CR13","doi-asserted-by":"publisher","first-page":"74218","DOI":"10.1109\/ACCESS.2021.3077901","volume":"9","author":"D Alsadie","year":"2021","unstructured":"Alsadie D (2021) A metaheuristic framework for dynamic virtual machine allocation with optimized task scheduling in cloud data centers. IEEE Access 9:74218\u201374233","journal-title":"IEEE Access"},{"issue":"4","key":"8769_CR14","first-page":"1041","volume":"12","author":"IM Ibrahim","year":"2021","unstructured":"Ibrahim IM, Radie A, Jacksi K, Zeebaree SR, Shukur HM, Rashid ZN, Sadeeq MA, Yasin HM (2021) Task scheduling algorithms in cloud computing: A review. Turkish J Comput Math Edu 12(4):1041\u20131053","journal-title":"Turkish J Comput Math Edu"},{"issue":"1","key":"8769_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-017-1044-2","volume":"52","author":"P Singh","year":"2017","unstructured":"Singh P, Dutta M, Aggarwal N (2017) A review of task scheduling based on meta-heuristics approach in cloud computing. Knowl Inf Syst 52(1):1\u201351","journal-title":"Knowl Inf Syst"},{"key":"8769_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2025.118348","volume":"447","author":"T Yang","year":"2025","unstructured":"Yang T, Qian Z, Hang N, Liu M (2025) S-pinn: Stabilized physics-informed neural networks for alleviating barriers between multi-level co-optimization. Comput Methods Appl Mech Eng 447:118348","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"1","key":"8769_CR17","first-page":"2163965","volume":"2021","author":"X Ding","year":"2021","unstructured":"Ding X, Zhang W (2021) Computing unloading strategy of massive internet of things devices based on game theory in mobile edge computing. Math Probl Eng 2021(1):2163965","journal-title":"Math Probl Eng"},{"key":"8769_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105345","volume":"116","author":"R Aron","year":"2022","unstructured":"Aron R, Abraham A (2022) Resource scheduling methods for cloud computing environment: The role of meta-heuristics and artificial intelligence. Eng Appl Artif Intell 116:105345","journal-title":"Eng Appl Artif Intell"},{"issue":"5","key":"8769_CR19","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1007\/s10462-024-10756-9","volume":"57","author":"G Zhou","year":"2024","unstructured":"Zhou G, Tian W, Buyya R, Xue R, Song L (2024) Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions. Artif Intell Rev 57(5):124","journal-title":"Artif Intell Rev"},{"key":"8769_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2026.103429","volume":"93","author":"J Duan","year":"2026","unstructured":"Duan J, Xiang J, Li Z, Xue L, Xiang W (2026) Dual-stream spatio-temporal gcn-transformer network for 3d human pose estimation. Displays 93:103429","journal-title":"Displays"},{"key":"8769_CR21","first-page":"1","volume":"72","author":"Q Li","year":"2023","unstructured":"Li Q, Luo H, Cheng H, Deng Y, Sun W, Li W, Liu Z (2023) Incipient fault detection in power distribution system: A time frequency embedded deep-learning-based approach. IEEE Trans Instrum Meas 72:1\u201314","journal-title":"IEEE Trans Instrum Meas"},{"issue":"3","key":"8769_CR22","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s10586-024-04828-2","volume":"28","author":"W Shen","year":"2025","unstructured":"Shen W, Lin W, Wu W, Wu H, Li K (2025) Reinforcement learning-based task scheduling for heterogeneous computing in end-edge-cloud environment. Clust Comput 28(3):179","journal-title":"Clust Comput"},{"issue":"1","key":"8769_CR23","first-page":"2023","volume":"74","author":"AY Hamed","year":"2023","unstructured":"Hamed AY, Elnahary MK, Alsubaei FS, El-Sayed HH (2023) Optimization task scheduling using cooperation search algorithm for heterogeneous cloud computing systems. Comput Mater Continua 74(1):2023","journal-title":"Comput Mater Continua"},{"issue":"12","key":"8769_CR24","first-page":"3477","volume":"97","author":"A Younes","year":"2019","unstructured":"Younes A, BenSalah A, Farag T, Alghamdi F, Badawi U (2019) Task scheduling algorithm for heterogeneous multi processing computing systems. J Theor Appl Inf Technol 97(12):3477\u20133487","journal-title":"J Theor Appl Inf Technol"},{"issue":"2","key":"8769_CR25","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.1109\/TSG.2025.3633994","volume":"17","author":"H Luo","year":"2026","unstructured":"Luo H, Li Q, Zhou H, Sun W, Li W, Liu Z, Ji Y, Ding L (2026) Weak single phase-to-ground fault time detection with learnable-parameter-driven dsp-enhanced transformer. IEEE Trans Smart Grid 17(2):1693\u20131708","journal-title":"IEEE Trans Smart Grid"},{"issue":"2","key":"8769_CR26","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1109\/TGCN.2021.3067374","volume":"5","author":"Y Huang","year":"2021","unstructured":"Huang Y, Xu H, Gao H, Ma X, Hussain W (2021) Ssur: an approach to optimizing virtual machine allocation strategy based on user requirements for cloud data center. IEEE Trans Green Commun Networking 5(2):670\u2013681","journal-title":"IEEE Trans Green Commun Networking"},{"key":"8769_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108653","volume":"107","author":"Y Song","year":"2023","unstructured":"Song Y, Li C, Tian L, Song H (2023) A reinforcement learning based job scheduling algorithm for heterogeneous computing environment. Comput Electr Eng 107:108653","journal-title":"Comput Electr Eng"},{"key":"8769_CR28","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.future.2022.11.032","volume":"141","author":"G Chen","year":"2023","unstructured":"Chen G, Qi J, Sun Y, Hu X, Dong Z, Sun Y (2023) A collaborative scheduling method for cloud computing heterogeneous workflows based on deep reinforcement learning. Futur Gener Comput Syst 141:284\u2013297","journal-title":"Futur Gener Comput Syst"},{"key":"8769_CR29","doi-asserted-by":"publisher","first-page":"2753","DOI":"10.1007\/s10586-019-03042-9","volume":"23","author":"Z Peng","year":"2020","unstructured":"Peng Z, Lin J, Cui D, Li Q, He J (2020) A multi-objective trade-off framework for cloud resource scheduling based on the deep q-network algorithm. Clust Comput 23:2753\u20132767","journal-title":"Clust Comput"},{"key":"8769_CR30","doi-asserted-by":"publisher","first-page":"2224","DOI":"10.1109\/ACCESS.2023.3347803","volume":"12","author":"Q Zhao","year":"2024","unstructured":"Zhao Q, Han J, Xu M (2024) Boosting policy learning in reinforcement learning via adaptive intrinsic reward regulation. IEEE Access 12:2224\u20132235","journal-title":"IEEE Access"},{"issue":"1","key":"8769_CR31","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s10586-021-03431-z","volume":"25","author":"A Katal","year":"2022","unstructured":"Katal A, Dahiya S, Choudhury T (2022) Energy efficiency in cloud computing data center: a survey on hardware technologies. Clust Comput 25(1):675\u2013705","journal-title":"Clust Comput"},{"issue":"8","key":"8769_CR32","doi-asserted-by":"publisher","first-page":"9113","DOI":"10.1109\/TII.2022.3225313","volume":"19","author":"S Dong","year":"2023","unstructured":"Dong S, Xia Y, Kamruzzaman J (2023) Quantum particle swarm optimization for task offloading in mobile edge computing. IEEE Trans Industr Inf 19(8):9113\u20139122","journal-title":"IEEE Trans Industr Inf"},{"key":"8769_CR33","first-page":"51603","volume":"13","author":"T Keyvan Atbaee","year":"2024","unstructured":"Keyvan Atbaee T, Homayun M, Behnam B, Ebrahim A, Hossien S, Mehran M (2024) Qos aware task scheduling using hybrid genetic algorithm in cloud computing. IEEE Access 13:51603\u201351616","journal-title":"IEEE Access"},{"key":"8769_CR34","unstructured":"Liang X, Xu C, Zhao Z, Zhang X, Wu Q (2020) Cloud computing resource scheduling based on ant colony optimization and simulated annealing algorithm. 2020 5th International Conference on Computer and Communication Systems (ICCCS). IEEE, pp 717\u2013722"},{"issue":"1","key":"8769_CR35","first-page":"1","volume":"31","author":"J Chen","year":"2025","unstructured":"Chen J, Zhang Q, Han P, Zhang Y, Lu Y, Zheng P (2025) Energy-aware scheduling of workflow applications towards schedule length optimization in heterogeneous distributed embedded systems. ACM Trans Des Autom Electron Syst 31(1):1\u201327","journal-title":"ACM Trans Des Autom Electron Syst"},{"issue":"10","key":"8769_CR36","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1049\/iet-com.2018.5202","volume":"13","author":"H Zhu","year":"2019","unstructured":"Zhu H, Xu M, Xu H (2019) Integrating power assignment into energy-efficient routing in e2e retransmission systems. IET Commun 13(10):1524\u20131529","journal-title":"IET Commun"},{"key":"8769_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2026.107383","volume":"143","author":"Y Xia","year":"2026","unstructured":"Xia Y, Zheng D, Zhang Y, Deng W, Yang T (2026) Balancing urban environmental and societal performance in high-density cities: Morphological trade-offs and optimization strategies in shenzhen and hong kong. Sustain Cities Soc 143:107383","journal-title":"Sustain Cities Soc"},{"issue":"6","key":"8769_CR38","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.3390\/math11061548","volume":"11","author":"K Shao","year":"2023","unstructured":"Shao K, Song Y, Wang B (2023) Pga: a new hybrid pso and ga method for task scheduling with deadline constraints in distributed computing. Mathematics 11(6):1548","journal-title":"Mathematics"},{"issue":"1","key":"8769_CR39","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/s40537-020-00321-w","volume":"7","author":"V Seethalakshmi","year":"2020","unstructured":"Seethalakshmi V, Govindasamy V, Akila V (2020) Hybrid gradient descent spider monkey optimization (hgdsmo) algorithm for efficient resource scheduling for big data processing in heterogenous environment. J Big Data 7(1):49","journal-title":"J Big Data"},{"issue":"2","key":"8769_CR40","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.fcij.2018.03.004","volume":"3","author":"S Srichandan","year":"2018","unstructured":"Srichandan S, Kumar TA, Bibhudatta S (2018) Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm. Future Comput Inform J 3(2):210\u2013230","journal-title":"Future Comput Inform J"},{"issue":"11","key":"8769_CR41","doi-asserted-by":"publisher","first-page":"3658","DOI":"10.1109\/TCYB.2016.2574766","volume":"47","author":"H Yuan","year":"2016","unstructured":"Yuan H, Bi J, Tan W, Zhou M, Li BH, Li J (2016) Ttsa: An effective scheduling approach for delay bounded tasks in hybrid clouds. IEEE Trans Cybernet 47(11):3658\u20133668","journal-title":"IEEE Trans Cybernet"},{"issue":"6","key":"8769_CR42","doi-asserted-by":"publisher","first-page":"9090","DOI":"10.1109\/TVT.2025.3639294","volume":"75","author":"J Chen","year":"2026","unstructured":"Chen J, Wang Y, Zhang Y, Lu Y, Shu Q, Hu Y, Zhou Y (2026) Pointer network-based reinforcement learning for uav-assisted mobile edge computing. IEEE Trans Veh Technol 75(6):9090\u20139102","journal-title":"IEEE Trans Veh Technol"},{"issue":"3","key":"8769_CR43","doi-asserted-by":"publisher","first-page":"8359","DOI":"10.1007\/s11042-023-16008-2","volume":"83","author":"S Mangalampalli","year":"2024","unstructured":"Mangalampalli S, Karri GR, Kumar M, Khalaf OI, Romero CAT, Sahib GA (2024) Drlbtsa: Deep reinforcement learning based task-scheduling algorithm in cloud computing. Multimedia tools and applications 83(3):8359\u20138387","journal-title":"Multimedia tools and applications"},{"issue":"7","key":"8769_CR44","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1109\/TPDS.2021.3124670","volume":"33","author":"MT Islam","year":"2021","unstructured":"Islam MT, Karunasekera S, Buyya R (2021) Performance and cost-efficient spark job scheduling based on deep reinforcement learning in cloud computing environments. IEEE Trans Parallel Distrib Syst 33(7):1695\u20131710","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"8769_CR45","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.future.2023.05.032","volume":"148","author":"J Liu","year":"2023","unstructured":"Liu J, Wu Z, Feng D, Zhang M, Wu X, Yao X, Yu D, Ma Y, Zhao F, Dou D (2023) Heterps: Distributed deep learning with reinforcement learning based scheduling in heterogeneous environments. Futur Gener Comput Syst 148:106\u2013117","journal-title":"Futur Gener Comput Syst"},{"key":"8769_CR46","doi-asserted-by":"publisher","first-page":"197863","DOI":"10.1109\/ACCESS.2020.3033557","volume":"8","author":"J Lin","year":"2020","unstructured":"Lin J, Cui D, Peng Z, Li Q, He J (2020) A two-stage framework for the multi-user multi-data center job scheduling and resource allocation. IEEE Access 8:197863\u2013197874","journal-title":"IEEE Access"},{"key":"8769_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2022.100531","volume":"24","author":"N Sharma","year":"2022","unstructured":"Sharma N, Garg P (2022) Ant colony based optimization model for qos-based task scheduling in cloud computing environment. Measurement Sensors 24:100531","journal-title":"Measurement Sensors"},{"key":"8769_CR48","doi-asserted-by":"crossref","unstructured":"Kaur R, Laxmi V (2022) Balkrishan: Performance evaluation of task scheduling algorithms in virtual cloud environment to minimize makespan. Int J Inf Technol 1\u201315","DOI":"10.1007\/s41870-021-00753-4"},{"issue":"1","key":"8769_CR49","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.32604\/cmc.2024.045660","volume":"78","author":"S Zhao","year":"2024","unstructured":"Zhao S, Lin Q, Feng X, Chen H, Zhang D (2024) Hybrid hierarchical particle swarm optimization with evolutionary artificial bee colony algorithm for task scheduling in cloud computing. Comput Mater Continua 78(1):1135\u20131156","journal-title":"Comput Mater Continua"},{"key":"8769_CR50","doi-asserted-by":"crossref","unstructured":"Meng Q, Gao Y, Hussain S, He Y, Li B (2026) A novel approach for high impedance fault detection in resonant grounding systems based on statistical characteristics. Sci Re","DOI":"10.1038\/s41598-025-33118-z"},{"issue":"6","key":"8769_CR51","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1109\/TPDS.2020.2968427","volume":"31","author":"D Yi","year":"2020","unstructured":"Yi D, Zhou X, Wen Y, Tan R (2020) Efficient compute-intensive job allocation in data centers via deep reinforcement learning. IEEE Trans Parallel Distrib Syst 31(6):1474\u20131485","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"8769_CR52","doi-asserted-by":"publisher","first-page":"16344","DOI":"10.52202\/068431-1189","volume":"35","author":"T Dao","year":"2022","unstructured":"Dao T, Fu DY, Ermon S, Rudra A, R\u00e9 C (2022) Flashattention: Fast and memory-efficient exact attention with io-awareness. Adv Neural Inf Process Syst 35:16344\u201316359","journal-title":"Adv Neural Inf Process Syst"},{"key":"8769_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2026.109134","volume":"15","author":"Q Meng","year":"2026","unstructured":"Meng Q, Tao W, Zhao L, She Q, Guo Q, Guo L, Gao Y, Wang S, Wang H, Long R et al (2026) High-impedance fault detection in resonant grounded distribution networks using multi-fractal detrended fluctuation analysis and an improved whale-optimized support vector machine. Energy Rep 15:109134","journal-title":"Energy Rep"},{"issue":"3","key":"8769_CR54","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1109\/TSC.2024.3351622","volume":"17","author":"Y Yang","year":"2024","unstructured":"Yang Y, Shen H, Tian H (2024) Scheduling workflow tasks with unknown task execution time by combining machine-learning and greedy-optimization. IEEE Trans Serv Comput 17(3):1181\u20131195","journal-title":"IEEE Trans Serv Comput"},{"key":"8769_CR55","unstructured":"Silva Filho MC (2024) Simulation and performance evaluation of a distributed virtual-machine placement and migration approach for management of cloud computing resources using cloudsim plus. Universidade da Beira Interior (Portugal,(Master&apos;s thesis)"},{"issue":"2","key":"8769_CR56","first-page":"5439","volume":"8","author":"MO Ahmad","year":"2019","unstructured":"Ahmad MO, Khan R (2019) Cloud computing modeling and simulation using cloudsim environment. Int J Recent Technol Eng 8(2):5439\u20135445","journal-title":"Int J Recent Technol Eng"},{"issue":"4","key":"8769_CR57","doi-asserted-by":"publisher","first-page":"286","DOI":"10.36548\/jismac.2023.4.001","volume":"5","author":"P Chapagai","year":"2023","unstructured":"Chapagai P, Shakya S (2023) Performance analysis of various scheduling algorithms using cloudsim. J IoT Soc Mobile Anal Cloud 5(4):286\u2013301","journal-title":"J IoT Soc Mobile Anal Cloud"},{"key":"8769_CR58","doi-asserted-by":"publisher","first-page":"86739","DOI":"10.1109\/ACCESS.2023.3305268","volume":"11","author":"Z Ma","year":"2023","unstructured":"Ma Z, Ma D, Lv M, Liu Y (2023) Virtual machine migration techniques for optimizing energy consumption in cloud data centers. IEEE Access 11:86739\u201386753","journal-title":"IEEE Access"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08769-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08769-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08769-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T08:02:17Z","timestamp":1788249737000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08769-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,1]]},"references-count":58,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["8769"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08769-y","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,1]]},"assertion":[{"value":"19 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declared that they have no conflict of interest regarding this work.","order":1,"name":"Ethics","label":"Conflicts of Interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies involving human participants or animals conducted by any of the authors.","order":2,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"688"}}