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A Q-learning approach for the autoscaling of scientific workflows in the Cloud.

, , and . Future Gener. Comput. Syst., (2022)

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