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Author Wei, Jie ♦ Zhou, Ao ♦ Yuan, Jie ♦ Yang, Fangchun
Editor Kobusinska, Anna
Source Hindawi
Content type Text
Publisher Hindawi
File Format PDF
Copyright Year ©2018
Language English
Abstract Federated-cloud has been widely deployed due to the growing popularity of real-time applications, and hence allocating resources among clouds becomes nontrivial to meet the stringent service requirements. The challenges lie in achieving minimized latency constrained by virtual machines rental overhead and resource requirement. This becomes further complicated by the issues of datacenter selection. To this end, we propose AIMING, a novel resource allocation approach which aims to minimize the latency constrained by monetary overhead in the context of federated-cloud. Specifically, the network resources are deployed and selected according to k-means clustering. Meanwhile, the total latency among datacenters is optimized based on binary quadratic programming. The evaluation is conducted with real data traces. The results show that AIMING can reduce total datacenter latency effectively compared with other approaches.
ISSN 15308669
Learning Resource Type Article
Publisher Date 2018-04-10
Rights License This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e-ISSN 15308677
Journal Wireless Communications and Mobile Computing
Volume Number 2018
Page Count 11


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