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Abstract
Data Centers usually host different third party applications, each of them possibly having different requirements in terms of QoS. To achieve them, sufficient resources, like CPU and memory, must be allocated to each application. However, workload fluctuations might arise, and so, resource demands will vary. Allocations based on worst/average case scenarios can lead to non-desirable results. A better approach is then to assign resources on demand. Also, due to the complexity and size of current and future systems, self-adaptive solutions are essential. In this paper, we then presentGrains, a self-adaptive approach for resource management in Data Centers under varying workload.
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Systems Research Group School of Computer Science and Informatics, University College Dublin, Dublin, Ireland
Emerson Loureiro, Paddy Nixon & Simon Dobson
- Emerson Loureiro
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- Paddy Nixon
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- Simon Dobson
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Editors and Affiliations
Institute of Distributed and Multimedia Systems, University of Vienna, Lenaugasse 2/8, A-1080, Vienna
Karin Anna Hummel
Coommunications and networking Systems laboratory Information Telecommunication Technology Center, Lancaster University, 145 Nichols Hall, Lawrance, 66045-7612, Kansas, USA
James P. G. Sterbenz
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Loureiro, E., Nixon, P., Dobson, S. (2008). A Fine-Grained Model for Adaptive On-Demand Provisioning of CPU Shares in Data Centers. In: Hummel, K.A., Sterbenz, J.P.G. (eds) Self-Organizing Systems. IWSOS 2008. Lecture Notes in Computer Science, vol 5343. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92157-8_9
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