A Fine-grained Model for Adaptive On-demand Provisioning of CPU Shares in Data Centers

E Loureiro; Paddy Nixon; S Dobson

doi:10.1007/978-3-540-92157-8_9

A Fine-grained Model for Adaptive On-demand Provisioning of CPU Shares in Data Centers

E Loureiro, Paddy Nixon, S Dobson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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 present Grains, a self-adaptive approach for resource management in Data Centers under varying workload.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	Springer
Pages	97-108
Number of pages	12
ISBN (Print)	978-3-540-92156-1
DOIs	https://doi.org/10.1007/978-3-540-92157-8_9
Publication status	Published (in print/issue) - 2008
Event	Self-Organizing Systems: Third International Workshop, IWSOS 2008 - Vienna, Austria Duration: 1 Jan 2008 → …

Workshop

Workshop	Self-Organizing Systems: Third International Workshop, IWSOS 2008
Period	1/01/08 → …

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n/a

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10.1007/978-3-540-92157-8_9

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title = "A Fine-grained Model for Adaptive On-demand Provisioning of CPU Shares in Data Centers",

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 present Grains, a self-adaptive approach for resource management in Data Centers under varying workload.",

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AU - Loureiro, E

AU - Nixon, Paddy

AU - Dobson, S

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N2 - 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 present Grains, a self-adaptive approach for resource management in Data Centers under varying workload.

AB - 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 present Grains, a self-adaptive approach for resource management in Data Centers under varying workload.

KW - n/a

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Y2 - 1 January 2008

ER -

A Fine-grained Model for Adaptive On-demand Provisioning of CPU Shares in Data Centers

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