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LOGOS: Enabling Local Resource Managers for the Efficient Support of Data-Intensive Workflows within Grid Sites., и . Comput. Informatics, 33 (1): 109-130 (2014)Reinforcement learning-based application Autoscaling in the Cloud: A survey., , , , и . Eng. Appl. Artif. Intell., (2021)Autoscaling scientific workflows on the cloud by combining on-demand and spot instances., , , и . Comput. Syst. Sci. Eng., (2017)Ensemble learning of runtime prediction models for gene-expression analysis workflows., , , и . Clust. Comput., 18 (4): 1317-1329 (2015)CMI: An online multi-objective genetic autoscaler for scientific and engineering workflows in cloud infrastructures with unreliable virtual machines., , , , и . J. Netw. Comput. Appl., (2020)Template-based semi-automatic workflow construction for gene expression data analysis., , , , и . CBMS, стр. 1-6. IEEE Computer Society, (2011)A performance comparison of data-aware heuristics for scheduling jobs in mobile grids., , , , , и . CLEI, стр. 1-8. IEEE, (2017)A Comparative Analysis of NSGA-II and NSGA-III for Autoscaling Parameter Sweep Experiments in the Cloud., , , , и . Sci. Program., (2020)Markov Decision Process to Dynamically Adapt Spots Instances Ratio on the Autoscaling of Scientific Workflows in the Cloud., , , и . CARLA, том 796 из Communications in Computer and Information Science, стр. 353-369. Springer, (2017)Adaptive Spot-Instances Aware Autoscaling for Scientific Workflows on the Cloud., и . CARLA, том 485 из Communications in Computer and Information Science, стр. 13-27. Springer, (2014)