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

Abstract : Performing efficient resource provisioning is a fundamental aspect for any resource provider. Local Resource Management Systems (LRMS) have been used in data centers for decades in order to obtain the best usage of the resources, providing their fair usage and partitioning for the users. In contrast, current cloud schedulers are normally based on the immediate allocation of resources on a first-come, first-served basis, meaning that a request will fail if there are no resources (e.g. OpenStack) or it will be trivially queued ordered by entry time (e.g. OpenNebula). Moreover, these scheduling strategies are based on a static partitioning of the resources, meaning that existing quotas cannot be exceeded, even if there are idle resources allocated to other projects. This is a consequence of the fact that cloud instances are not associated with a maximum execution time and leads to a situation where the resources are under-utilized. These facts have been identified by the INDIGO-DataCloud project as being too simplistic for accommodating scientific workloads in an efficient way, leading to an underutilization of the resources, a non desirable situation in scientific data centers. In this work, we will present the work done in the scheduling area during the first year of the INDIGO project and the foreseen evolutions.
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Communication dans un congrès
22nd International Conference on Computing in High Energy and Nuclear Physics, Oct 2016, San Francisco, United States. J.Phys.Conf.Ser., 898 (9), pp.092010, 2017, 〈10.1088/1742-6596/898/9/092010〉
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https://hal.archives-ouvertes.fr/hal-01669630
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Soumis le : mercredi 20 décembre 2017 - 23:47:44
Dernière modification le : jeudi 11 janvier 2018 - 06:15:13

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Alvaro Lopez Garcia, Lisa Zangrando, Massimo Sgaravatto, Vincent Llorens, Sara Vallero, et al.. Improved Cloud resource allocation: how INDIGO-DataCloud is overcoming the current limitations in Cloud schedulers. 22nd International Conference on Computing in High Energy and Nuclear Physics, Oct 2016, San Francisco, United States. J.Phys.Conf.Ser., 898 (9), pp.092010, 2017, 〈10.1088/1742-6596/898/9/092010〉. 〈hal-01669630〉

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