Scarlett: Coping with Skewed Popularity Content in MapReduce Clusters

To improve data availability and resilienceMapReduce frame-works use le systems that replicate data uniformly.However, analysis of job logs from a large production cluster shows wide disparity in data popularity.Machines and racks storing popular content become bottlenecks; thereby increasing the completion times of jobs accessing this data even when there are machines with spare cycles in the cluster. To address this
problem, we present Scarlett, a system that replicates blocks based on their popularity. By accurately predicting le popularity and working within hard bounds on additional storage, Scarlett causes minimal interference to running jobs. Trace driven simulations and experiments in two popular MapReduce frameworks (Hadoop and Dryad) show that Scarlett effectively alleviates hotspots and can speed up jobs by 20.2%.