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Tarema: Adaptive Resource Allocation for Scalable Scientific Workflows in Heterogeneous Clusters., , , , , и . IEEE BigData, стр. 65-75. IEEE, (2021)Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts., , , , , , и . CLUSTER, стр. 261-270. IEEE, (2021)Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation., , , , , , и . IPCCC, стр. 1-8. IEEE, (2021)Selecting Efficient Cluster Resources for Data Analytics: When and How to Allocate for In-Memory Processing?, , , и . SSDBM, стр. 24:1-24:4. ACM, (2023)Towards a Peer-to-Peer Data Distribution Layer for Efficient and Collaborative Resource Optimization of Distributed Dataflow Applications., , , , и . IEEE Big Data, стр. 2339-2345. IEEE, (2023)Get Your Memory Right: The Crispy Resource Allocation Assistant for Large-Scale Data Processing., , , , и . IC2E, стр. 58-66. IEEE, (2022)Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling., , , , , и . ICPE (Companion), стр. 269-272. ACM, (2024)Ruya: Memory-Aware Iterative Optimization of Cluster Configurations for Big Data Processing., , , , и . IEEE Big Data, стр. 161-169. IEEE, (2022)Training Data Reduction for Performance Models of Data Analytics Jobs in the Cloud., , , , и . IEEE BigData, стр. 3141-3146. IEEE, (2021)On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds., , , , , и . IEEE BigData, стр. 3113-3118. IEEE, (2021)