  Large multi-tenant production clusters often have to handle a variety of jobs
and applications with a variety of complex resource usage characteristics. It
is non-trivial and non-optimal to manually create placement rules for
scheduling that would decide which applications should co-locate. In this
paper, we present DeepPlace, a scheduler that learns to exploits various
temporal resource usage patterns of applications using Deep Reinforcement
Learning (Deep RL) to reduce resource competition across jobs running in the
same machine while at the same time optimizing for overall cluster utilization.
