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F. Jrad, J. Tao, I. Brandic, A. Streit:
"Multi-dimensional Resource Allocation for Data-intensive Large-scale Cloud Applications";
Talk: 4th International Conference on Cloud Computing and Services Science, CLOSER 2014, Special Session on Multi-Clouds, Barcelona, Spain; 2014-04-03 - 2014-04-05; in: "CLOSER 2014 - Proceedings of the 4th International Conference on Cloud Computing and Services Science", M Helfert, F. Desprez, D. Ferguson, F. Leymann, V. Munoz (ed.); SciTePress, (2014), ISBN: 978-989-758-019-2; 691 - 702.

Large scale applications are emerged as one of the important applications in distributed computing. Today, the economic and technical benefits offered by the Cloud computing technology encouraged many users to migrate their applications to Cloud. On the other hand, the variety of the existing Clouds requires them to make decisions about which providers to choose in order to achieve the expected performance and service quality while keeping the payment low. In this paper, we present a multi-dimensional resource allocation scheme to automate the deployment of data-intensive large scale applications in Multi-Cloud environments. The scheme applies a two level approach in which the target Clouds are matched with respect to the Service Level Agreement (SLA) requirements and user payment at first and then the application workloads are distributed to the selected Clouds using a data locality driven scheduling policy. Using an implemented Multi-Cloud simulation environment, we evaluated our approach with a real data-intensive workflow application in different scenarios. The experimental results demonstrate the effectiveness of the implemented matching and scheduling policies in improving the workflow execution performance and reducing the amount and costs of Intercloud data transfers.

Cloud Computing, Multi-Cloud, Resource Allocation, Scientific Workflow, Data Locality.

http://dx.doi.org/10.5220/0004971906910702