Heterogeneous cores for MapReduce processing: Opportunity or challenge?

To offer diverse computing capabilities, the emergent modern system on a chip (SoC) might include heterogeneous multi-core processors. The current SoC design is often constrained by a given power budget that forces designers to consider different decision trade-offs, e.g., to choose between many slow cores, fewer faster cores, or to select a combination of them. In this work, we design a new Hadoop scheduler, called DyScale, that exploits capabilities offered by heterogeneous cores for achieving a variety of performance objectives. Our preliminary performance evaluation results confirm potential benefits of heterogeneous multi-core processors for “faster” processing of the small, interactive MapReduce jobs, while at the same time offering an improved throughput and performance for large, batch job processing.