In OpenStack, the current resources allocation model provides to each user group a
fixed amount of resources. This model based on fixed quotas, accurately reflects the
economic model, pay-per-use, on which the Cloud paradigm is built. However it is not
pretty suited to the computational model of the scientific computing whose demands of
resources consumption can not be predetermined, but vary greatly in time. Usually the
size of the quota is agreed with the Cloud Infrastructure manager, contextually with the
creation of a new project and it just rarely changes over the time. The main limitation
due to the static partitioning of resources occurs mainly in a scenario of full quota
utilization. In this context, the project can not exceed its own quota even if, in the cloud
infrastructure, there are several unused resources but assigned to different groups. It
follows that the overall efficiency in a Data Centre is often rather low.

The European project INDIGO DataCloud is addressing this issue with “Synergy”, a
new service that provides to OpenStack an advanced provisioning model based on
scheduling algorithms known by the name of “fair-share”. In addition to maximizing
the usage, the fair-share ensures that these resources are equitably distributed between
users and groups.

In this paper will be discussed the solution offered by INDIGO with Synergy, by
describing its features, architecture and the selected algorithm limitations confirmed
by the preliminary results of tests performed in the Padua testbed integrated with EGI
Federated Cloud.