Configuring in-memory cluster computing using random forest.

Recently, in-memory cluster computing (IMC) gains momentum because it accelerates traditional on-disk cluster computing (ODC) up to several tens of times for iterative and interaction applications. The most popular IMC framework is Spark and it has more than 100 configuration parameters. However, it is unclear how significantly these parameters affect the system performance because IMC is a quite new computing paradigm. Consequently, there is yet no study addressing how to optimally configure IMC frameworks.