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Tarema: Adaptive Resource Allocation for Scalable Scientific Workflows in Heterogeneous Clusters., , , , , and . IEEE BigData, page 65-75. IEEE, (2021)Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts., , , , , , and . CLUSTER, page 261-270. IEEE, (2021)Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics., , , , , and . CoRR, (2023)Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation., , , , , , and . IPCCC, page 1-8. IEEE, (2021)Towards a Peer-to-Peer Data Distribution Layer for Efficient and Collaborative Resource Optimization of Distributed Dataflow Applications., , , , and . IEEE Big Data, page 2339-2345. IEEE, (2023)Selecting Efficient Cluster Resources for Data Analytics: When and How to Allocate for In-Memory Processing?, , , and . SSDBM, page 24:1-24:4. ACM, (2023)Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling., , , , , and . CoRR, (2024)Ruya: Memory-Aware Iterative Optimization of Cluster Configurations for Big Data Processing., , , , and . IEEE Big Data, page 161-169. IEEE, (2022)Get Your Memory Right: The Crispy Resource Allocation Assistant for Large-Scale Data Processing., , , , and . IC2E, page 58-66. IEEE, (2022)Collaborative Cluster Configuration for Distributed Data-Parallel Processing: A Research Overview., , , , and . CoRR, (2022)