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Performance and Cost Analysis between Elasticity Strategies over Pipeline-structured Applications., , , , и . CLOSER, стр. 404-411. SciTePress, (2019)An Interference-Aware Application Classifier Based on Machine Learning to Improve Scheduling in Clouds., , , и . PDP, стр. 80-87. IEEE, (2020)Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Jobs., , , , , , , и . Euro-Par Workshops, том 11997 из Lecture Notes in Computer Science, стр. 519-530. Springer, (2019)Evaluating the performance and improving the usability of parallel and distributed Word Embeddings tools., , , , , и . PDP, стр. 201-206. IEEE, (2020)Modeling and Simulation of QoS-Aware Power Budgeting in Cloud Data Centers., , , , , , и . PDP, стр. 88-93. IEEE, (2020)IADA: A dynamic interference-aware cloud scheduling architecture for latency-sensitive workloads., , , и . J. Syst. Softw., (2022)Simulators Usage Analysis to Estimate Power Consumption in Cloud Computing Environments., , , , и . WSCAD, стр. 70-76. IEEE, (2018)IntP: Quantifying cross-application interference via system-level instrumentation., , , и . SBAC-PAD, стр. 231-240. IEEE, (2022)Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Jobs., , , , , , , и . CoRR, (2021)ML-driven classification scheme for dynamic interference-aware resource scheduling in cloud infrastructures., , , и . J. Syst. Archit., (2021)