Dask.distributed cluster administration
Usually people use a cluster manager like Kubernetes, Yarn, SLURM, SGE, PBS or something else.  That system handles user authentication, resource management, and so on.  A user then uses the one of the Dask-kubernetes, Dask-yarn, Dask-jobqueue projects to create their own short-lived scheduler and workers on the cluster on an as-needed basis.
I'm setting up Dask Python cluster at work (30 machines, 8 cores each in average). People use only a portion of their CPU power, so dask-workers will be running on background at low priority. All workers are listening to dask-scheduler on my master node. It works perfect if only I who use it, however it's gonna be used by several people in a concurrent manner - so i need to be able to admin this cluster:

Authenticate users, reject unknowns
Identify who submitted which jobs
Restrict number of submitted jobs per user
Restrict timeout for computation per job
Kill any job as admin

dask.distributed out of box provides a little of functionality described above. Could you please advice on some solution (may be hybrid Dask + something)?