Apache Mesos and Hadoop YARN Scheduling

Mesos and YARN are two powerful cluster managers that can play host to a variety of distributed programming frameworks (Hadoop Map-Reduce, Dryad, Spark, and Storm) as well as multiple instance of the same framework (e.g., different versions of Hadoop). Both are concerned about optimizing utilization of cluster resources especially in terms of data locality of data distributed around the cluster. Google's paper on Omega, their own cluster scheduling system, dubs Mesos a two-level scheduler, which provide some flexibility by having a single resource manager offer resources to multiple parallel, independent schedulers. YARN is considered a monolithic scheduler since independent Application Masters are only responsible for job management and not scheduling. Scheduling is the essence of efficient Big Data processing. However, where do these two systems differ?