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Performance-Feedback Autoscaling with Budget Constraints for Cloud-based Workloads of Workflows., , , , and . CoRR, (2019)An Elasticity Study of Distributed Graph Processing., , , and . CCGrid, page 382-383. IEEE Computer Society, (2018)Towards a Realistic Scheduler for Mixed Workloads with Workflows., and . CCGRID, page 753-756. IEEE Computer Society, (2015)An Experimental Performance Evaluation of Autoscalers for Complex Workflows., , , , , , and . ACM Trans. Model. Perform. Evaluation Comput. Syst., 3 (2): 8:1-8:32 (2018)The Impact of Task Runtime Estimate Accuracy on Scheduling Workloads of Workflows., and . CCGrid, page 331-341. IEEE Computer Society, (2018)Scheduling Workloads of Workflows in Clusters and Clouds.. Delft University of Technology, Netherlands, (2019)base-search.net (fttudelft:oai:tudelft.nl:uuid:18b7ff8a-6e70-4c9b-8b8e-c5c595fd40ea).Ananke: A Q-Learning-Based Portfolio Scheduler for Complex Industrial Workflows., , , and . ICAC, page 227-232. IEEE Computer Society, (2017)Elasticity in Graph Analytics? A Benchmarking Framework for Elastic Graph Processing., , , and . CLUSTER, page 381-391. IEEE Computer Society, (2018)Chamulteon: Coordinated Auto-Scaling of Micro-Services., , , , , and . ICDCS, page 2015-2025. IEEE, (2019)