Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud

Gunho Lee, Byung-Gon Chun, Randy H. Katz
University of California, Berkeley
3rd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud’11), 2011


   title={Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud},

   author={Lee, G. and Chun, B.G. and Katz, R.H.},

   journal={Proceedings of HotCloud},



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Data analytics are key applications running in the cloud computing environment. To improve performance and cost-effectiveness of a data analytics cluster in the cloud, the data analytics system should account for heterogeneity of the environment and workloads. In addition, it also needs to provide fairness among jobs when multiple jobs share the cluster. In this paper, we rethink resource allocation and job scheduling on a data analytics system in the cloud to embrace the heterogeneity of the underlying platforms and workloads. To that end, we suggest an architecture to allocate resources to a data analytics cluster in the cloud, and propose a metric of share in a heterogeneous cluster to realize a scheduling scheme that achieves high performance and fairness.
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