Optimizing the resource usage in Cloud based environments: the Synergy approach

Abstract : Managing resource allocation in a cloud based data centre serving multiple virtual organizations is a challenging issue. In fact, while batch systems are able to allocate resources to different user groups according to specific shares imposed by the data centre administrator, without a static partitioning of such resources, this is not so straightforward in the most common cloud frameworks, e.g. OpenStack. In the current OpenStack implementation, it is only possible to grant fixed quotas to the different user groups and these resources cannot be exceeded by one group even if there are unused resources allocated to other groups. Moreover in the existing OpenStack implementation, when there aren’t resources available, new requests are simply rejected: it is then up to the client to later re-issue the request. The recently started EU-funded INDIGO-DataCloud project is addressing this issue through “Synergy”, a new advanced scheduling service targeted for OpenStack. Synergy adopts a fair-share model for resource provisioning which guarantees that resources are distributed among users following the fair-share policies defined by the administrator, taken also into account the past usage of such resources. We present the architecture of Synergy, the status of its implementation, some preliminary results and the foreseen evolution of the service.
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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.092012, 2017, 〈10.1088/1742-6596/898/9/092012〉
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https://hal.archives-ouvertes.fr/hal-01669631
Contributeur : Inspire Hep <>
Soumis le : mercredi 20 décembre 2017 - 23:47:50
Dernière modification le : jeudi 11 janvier 2018 - 06:15:13

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L. Zangrando, V. Llorens, M. Sgaravatto, M. Verlato. Optimizing the resource usage in Cloud based environments: the Synergy approach. 22nd International Conference on Computing in High Energy and Nuclear Physics, Oct 2016, San Francisco, United States. J.Phys.Conf.Ser., 898 (9), pp.092012, 2017, 〈10.1088/1742-6596/898/9/092012〉. 〈hal-01669631〉

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