MRONLINE
Min Li, Liangzhao Zeng, Shicong Meng, Jian Rong Tan, Li Zhang, Ali Raza Butt, Nicholas Fuller · 2014
MapReduce job parameter tuning is a daunting and time consuming task. The parameter configuration space is huge; there are more than 70 parameters that impact job performance. It is also difficult for users to determine suitable values for the parameters without first having a good understanding of the MapReduce application characteristics. Thus, it is a challenge to systematically explore the parameter space and select a near-optimal configuration. Extant offline tuning approaches are slow and inefficient as they entail multiple test runs and significant human effort.