Abstract | ||
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Elastic resource provisioning is a key feature of cloud computing, allowing users to scale up or down resource allocation for their applications at run-time. To date, most practical approaches to managing elasticity are based on allocation/de-allocation of the virtual machine (VM) instances to the application. This VM-level elasticity typically incurs both considerable overhead and extra costs, especially for applications with rapidly fluctuating demands. In this paper, we propose a lightweight approach to enable cost-effective elasticity for cloud applications. Our approach operates fine-grained scaling at the resource level itself (CPUs, memory, I/O, etc) in addition to VM-level scaling. We also present the design and implementation of an intelligent platform for light-weight resource management of cloud applications. We describe our algorithms for light-weight scaling and VM-level scaling and show their interaction. We then use an industry standard benchmark to evaluate the effectiveness of our approach and compare its performance against traditional approaches. |
Year | DOI | Venue |
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2012 | 10.1109/CCGrid.2012.52 | CCGrid |
Keywords | Field | DocType |
vm-level elasticity,resource deallocation,lightweight resource scaling,lightweight scaling,resource allocation algorithms,fine grain scaling,cloud application,elastic resource,virtual machine,cloud applications,cost effective elasticity,light-weight resource management,industry standard benchmark,virtual machines,cost-effective elasticity,resource level,resource allocation,vm level scaling,light-weight scaling,cloud computing,lightweight resource management,vm-level scaling,resource manager,resource management,cost effectiveness,quality of service,servers,databases | Resource management,Virtual machine,Computer science,Real-time computing,Provisioning,Resource allocation,Elasticity (economics),Scaling,Industry standard,Distributed computing,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-4673-1395-7 | 47 | 1.49 |
References | Authors | |
13 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Rui Han | 1 | 74 | 11.51 |
guo li | 2 | 335 | 14.64 |
Moustafa Ghanem | 3 | 538 | 53.05 |
Yike Guo | 4 | 1319 | 165.32 |