An Adaptive Efficiency-Fairness Meta-Scheduler for Data-Intensive Computing

In data-intensive cluster computing platforms such as Hadoop YARN, efficiency and fairness are two important factors for system design and optimizations. Previous studies are either for efficiency or for fairness solely, without considering the tradeoff between efficiency and fairness. Recent studie...

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Veröffentlicht in:	IEEE transactions on services computing 2019-11, Vol.12 (6), p.865-879
Hauptverfasser:	Niu, Zhaojie, Tang, Shanjiang, He, Bingsheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models Clusters Computation Computer simulation data-intensive Design factors Design optimization Efficiency efficiency-fairness tradeoff Flexible printed circuits Google Hadoop YARN Machine learning Meta-scheduling Optimization Resource management Scheduling algorithms Simulation Systems design Task scheduling Tradeoffs Workload
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