该文档来自Spark Summit 2013峰会上来自UC Berkeley AMPLab的Kay Ousterhout的主题演讲。The current Spark scheduler relies on a single, centralized machine to make all scheduling decisions. However, as Spark is used on larger clusters and for shorter queries, the centralized scheduler will become a bottleneck.