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As a new promising paradigm, cloud computing can make good use of economics of scale and elastically deliver almost any IT
related services on demand. Nevertheless, one of the key problems remaining in cloud computing is related to virtual machine
images, which require a great amount of space/time to reposit/provision, especially with diverse requests from thousands of
users simultaneously. In this paper, by using the splitting and eliminating redundant data techniques, a space and time efficient
approach for virtual machines is proposed. The experiments demonstrate that, compared with existing solutions, our approach
can conserve more disk space and speed up the provisioning of virtual machines. 相似文献
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With the rapid growth of service scale, there are many services with the same functional properties but different non-functional
properties on the Internet. There have been some global optimizing service selection algorithms for service selection. However,
most of those approaches cannot fully reflect users’ preferences or are not fully suitable for large-scale services selection.
In this paper, an ant colony optimization (ACO) algorithm for the model of global optimizing service selection with various
quality of srevice (QoS) properties is employed, and a user-preference based large-scale service selection algorithm is proposed.
This algorithm aims at optimizing user-preferred QoS properties and selecting services that meet all user-defined QoS thresholds.
Experiment results prove that this algorithm is very efficient in this regard. 相似文献
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