MRONLINE

TLDR

MapReduce job parameter tuning is daunting due to a vast configuration space of over 70 parameters and the difficulty of selecting suitable values without deep application knowledge, making existing offline approaches slow and inefficient. The study aims to systematically explore the parameter space to select a near‑optimal configuration.

Abstract

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.