That would be awesome. We have seen some really disparate Mesos allocations for our Spark Streaming jobs. (like (7,4,1) over 3 executors for 4 kafka consumer instead of the ideal (3,3,3,3))
For network dependent consumers, achieving an even deployment would provide a reliable and reproducible streaming job execution from the performance point of view.
We're deploying in coarse grain mode. Not sure Spark Streaming would work well in fine-grained given the added latency to acquire a worker.
You mention that you're changing the Mesos scheduler. Is there a Jira where this job is taking place?