  Nowadays, data-centers are largely under-utilized because resource allocation
is based on reservation mechanisms which ignore actual resource utilization.
Indeed, it is common to reserve resources for peak demand, which may occur only
for a small portion of the application life time. As a consequence, cluster
resources often go under-utilized.
  In this work, we propose a mechanism that improves cluster utilization, thus
decreasing the average turnaround time, while preventing application failures
due to contention in accessing finite resources such as RAM. Our approach
monitors resource utilization and employs a data-driven approach to resource
demand forecasting, featuring quantification of uncertainty in the predictions.
Using demand forecast and its confidence, our mechanism modulates cluster
resources assigned to running applications, and reduces the turnaround time by
more than one order of magnitude while keeping application failures under
control. Thus, tenants enjoy a responsive system and providers benefit from an
efficient cluster utilization.

Distributed, Parallel, and Cluster Computing