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Performing efficient resource provisioning is a fundamental aspect for anyresource provider. Local Resource Management Systems (LRMS) have been used indata centers for decades in order to obtain the best usage of the resources,providing their fair usage and partitioning for the users. In contrast, currentcloud schedulers are normally based on the immediate allocation of resources ona first-come, first-served basis, meaning that a request will fail if there areno resources (e.g. OpenStack) or it will be trivially queued ordered by entrytime (e.g. OpenNebula). Moreover, these scheduling strategies are based on astatic partitioning of the resources, meaning that existing quotas cannot beexceeded, even if there are idle resources allocated to other projects. This isa consequence of the fact that cloud instances are not associated with amaximum execution time and leads to a situation where the resources areunder-utilized. These facts have been identified by the INDIGO-DataCloudproject as being too simplistic for accommodating scientific workloads in anefficient way, leading to an underutilization of the resources, a non desirablesituation in scientific data centers. In this work, we will present the workdone in the scheduling area during the first year of the INDIGO project and theforeseen evolutions.

Improved Cloud resource allocation: how INDIGO-DataCloud is overcoming the current limitations in Cloud schedulers