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Continuously Improving the Resource Utilization of Iterative Parallel Dataflows.

, , and . ICDCS Workshops, page 1-6. IEEE Computer Society, (2016)

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Continuously Improving the Resource Utilization of Iterative Parallel Dataflows., , and . ICDCS Workshops, page 1-6. IEEE Computer Society, (2016)CoBell: Runtime Prediction for Distributed Dataflow Jobs in Shared Clusters., , , and . CloudCom, page 81-88. IEEE Computer Society, (2018)Let's wait awhile: how temporal workload shifting can reduce carbon emissions in the cloud., , , , and . Middleware, page 260-272. ACM, (2021)SMiPE: Estimating the Progress of Recurring Iterative Distributed Dataflows., , , and . PDCAT, page 156-163. IEEE Computer Society, (2017)Lively groups: shared behavior in a world of objects without classes or prototypes., , , and . FPW@SPLASH, page 15-22. ACM, (2015)CoLoc: Distributed data and container colocation for data-intensive applications., , and . BigData, page 3008-3015. IEEE Computer Society, (2016)Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation., , , , , , and . IPCCC, page 1-8. IEEE, (2021)Dynamic resource allocation for distributed dataflows.. Technical University of Berlin, Germany, (2018)Multilayer Active Learning for Efficient Learning and Resource Usage in Distributed IoT Architectures., , , and . EDGE, page 8-12. IEEE, (2019)Implicit Parallelism through Deep Language Embedding., , , , , , , and . SIGMOD Conference, page 47-61. ACM, (2015)