Job Scheduling for Multi-User MapReduce Clusters

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

GreenHadoop: leveraging green energy in data-processing frameworks (2012, background): “FaceD is a scaled-down version of the workload studied in [33] because our cluster is significantly smaller.”
Domain-specific Genetic Algorithm for Multi-tenant DNNAccelerator Scheduling (2021, background): “Multitenancy has been investigated for decades for multi-tasking on a single CPU and job ordering in CPU clusters [96, 121].”
Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, background): “How can a large, shared resource support several users simultaneously in a predictable, transparent, policy-driven fashion? There has been some recent work along these lines in the context of Hadoop [131, 160].”
Data warehousing and analytics infrastructure at facebook (2010, methodology): “In order to support co-existence of interactive jobs and batch jobs on the same Hadoop cluster, Facebook has been instrumental in the development of the Hadoop Fair Share Scheduler [10] and we use it in all our clusters.”
Hopper: Decentralized Speculation-aware Cluster Scheduling at Scale (2015, background): “This overview highlights the lack of coordination that currently exists between scheduling and straggler mitigation strategies such as speculation.”