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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wang Xing-wei Wang Xue-yi Huang Min |
| Copyright Year | 2012 |
| Description | Author affiliation: College of Software, Northeastern University, Shenyang, China (Wang Xue-yi) || College of Information Science & Engineering, Northeastern University, Shenyang, China (Wang Xing-wei; Huang Min) |
| Abstract | On account of the resource characteristics under cloud computing environment and the flexibility and availability of applying economic mechanism to resource allocation, a resource allocation model based on the limited English combinatorial auction under cloud computing environment is advanced. An improved periodical auction model is constructed, and then ribbon capacity is adopted to describe the special storage capacity for special users. Based on the above, the resource trading price between resource buyers and resource providers is determined based on the limited English combinatorial auction model. In the end, the optimal resource allocation solution is pursued based on genetic algorithm. Simulation results have shown that the proposed model is both feasible and effective, which can maximize the seller total trading amounts as well as reduce the executing time of winner determination. |
| Starting Page | 905 |
| Ending Page | 909 |
| File Size | 304410 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781467300254 |
| e-ISBN | 9781467300247 |
| DOI | 10.1109/FSKD.2012.6234231 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-29 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | genetic algorithm Cloud computing resource allocation Computational modeling ribbon capacity limited English combinatorial auction Educational institutions Resource management cloud computing Biological cells Optimization Genetic algorithms |
| Content Type | Text |
| Resource Type | Article |
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