Finding the big data sweet spot: Towards automatically recommending configurations for hadoop clusters on docker containers

Abstract

The complexity of cloud-based analytics environments threatens to undermine their otherwise tremendous values. In particular, configuring such environments presents a great challenge. We propose to alleviate this issue with an engine that recommends configurations for a newly submitted analytics job in an intelligent and timely manner. The engine is rooted in a modified k-nearest neighbor algorithm, which finds desirable configurations from similar past jobs that have performed well. We apply the method to configuring an important class of analytics environments: Hadoop on container-driven clouds. Preliminary evaluation suggests up to 28% performance gain could result from our method.