  As resource estimation for jobs is difficult, users often overestimate their
requirements. Both commercial clouds and academic campus clusters suffer from
low resource utilization and long wait times as the resource estimates for
jobs, provided by users, is inaccurate. We present an approach to statistically
estimate the actual resource requirement of a job in a Little cluster before
the run in a Big cluster. The initial estimation on the little cluster gives us
a view of how much actual resources a job requires. This initial estimate
allows us to accurately allocate resources for the pending jobs in the queue
and thereby improve throughput and resource utilization. In our experiments, we
determined resource utilization estimates with an average accuracy of 90% for
memory and 94% for CPU, while we make better utilization of memory by an
average of 22% and CPU by 53%, compared to the default job submission methods
on Apache Aurora and Apache Mesos.
