Name
Abstract | ||
|---|---|---|
With the popularity of cloud computing technology, service hosting is used as a typical model to deploy different kinds of services on cloud platform. In recent years, how to effectively provide resources for service hosting has attracted more and more attention. However, most of the existing works only focused on how to effectively provide virtual machines for service hosting. They ignored how to efficiently place these virtual machines into physical servers, when considering multidimensional resource requirements. This may result in unreasonable virtual machine placement in servers, thereby causing the underutilization of resource. To address this problem, we propose a novel resource provisioning method including virtual machine provisioning for hosting service and virtual machine placement in servers. The proposed method decides how many virtual machines should be provided for each service by utilizing queuing theory. Then based on the virtual machines to be provided, the proposed method models the virtual machine placement problem as a variant of cutting stock problem, and decides how many servers should be provided by solving this problem. The proposed method is evaluated by simulations. Experimental results show the proposed method achieves a better performance than these baseline methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CSCWD.2016.7566011 | 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) |
Keywords | Field | DocType |
Resource provisioning,Multidimensional resource requirements,Virtual machine placement,Service hosting,Cloud platform | Virtual machine,Computer science,Server,Quality of service,Provisioning,Queueing theory,Cutting stock problem,Virtual machining,Distributed computing,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-5090-1916-8 | 1 | 0.34 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiyuan Shi | 1 | 19 | 2.38 |
| Fang Dong | 2 | 202 | 35.44 |
| Zhang Jinghui | 3 | 33 | 7.47 |
| Jin Jiahui | 4 | 88 | 16.84 |
| Junzhou Luo | 5 | 1257 | 153.97 |