Aggressive Resource Provisioning for Ensuring QoS in Virtualized Environments
Elasticity has now become the elemental feature of cloud computing as it enables the ability to dynamically add or remove virtual machine instances when workload changes. However, effective virtualized resource management is still one of the most challenging tasks. When the workload of a service inc...
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Veröffentlicht in: | IEEE transactions on cloud computing 2015-04, Vol.3 (2), p.119-131 |
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Zusammenfassung: | Elasticity has now become the elemental feature of cloud computing as it enables the ability to dynamically add or remove virtual machine instances when workload changes. However, effective virtualized resource management is still one of the most challenging tasks. When the workload of a service increases rapidly, existing approaches cannot respond to the growing performance requirement efficiently because of either inaccuracy of adaptation decisions or the slow process of adjustments, both of which may result in insufficient resource provisioning. As a consequence, the Quality of Service (QoS) of the hosted applications may degrade and the Service Level Objective (SLO) will be thus violated. In this paper, we introduce SPRNT, a novel resource management framework, to ensure high-level QoS in the cloud computing system. SPRNT utilizes an aggressive resource provisioning strategy which encourages SPRNT to substantially increase the resource allocation in each adaptation cycle when workload increases. This strategy first provisions resources which are possibly more than actual demands, and then reduces the over-provisioned resources if needed. By applying the aggressive strategy, SPRNT can satisfy the increasing performance requirement in the first place so that the QoS can be kept at a high level. The experimental results show that SPRNT achieves up to 7.7× speedup in adaptation time, compared with existing efforts. By enabling quick adaptation, SPRNT limits the SLO violation rate up to 1.3 percent even when dealing with rapidly increasing workload. |
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ISSN: | 2168-7161 2168-7161 2372-0018 |
DOI: | 10.1109/TCC.2014.2353045 |