Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud
Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud
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发表时间：
2011-06
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通讯作者：
Gunho Lee;R. Katz
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作者：
Gunho Lee;R. Katz
数据分析是在云计算环境中运行的关键应用程序。为了提高数据分析集群在云中的性能和成本效益，数据分析系统应考虑环境和工作量的异质性。此外，当多个工作共享集群时，它还需要在工作之间提供公平性。在本文中，我们重新考虑云中数据分析系统上的资源分配和工作调度，以包含基础平台和工作负载的异质性。为此，我们建议一个架构将资源分配给云中的数据分析集群，并提出一个异质集群中的共享度量，以实现达到高性能和公平性的调度方案。
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.