Institutional Repository
| Proactive service selection based on acquaintance model and LS-SVM | |
| Hu, JJ; Chen, XL; Zhang, CY | |
| 2016 | |
| Source | NEUROCOMPUTING
 |
| ISSN | 0925-2312 |
| Volume | 211Pages:60-65 |
| English Abstract | Current service selection is unable to perform proactively. When a service provider overloads, the services list is ever-lengthening, which leads to backlog and failure of service composition. To compensate for this deficiency, this paper improves the proactive service selection. In this strategy, the service provider analyses a time series of services received to forecast the backlog and consign services to the others through a negotiation process. The least squares support vector learning is used to predict a random list of services, and an acquaintance model (AM) makes a consigner allocate backlog services to other providers with high degree of relationship. The backlog of services by forecasting is entrusted to the provider who can implement the same service, and negotiation between the providers with the same role would allow generation of a new service selection solution before a fault occurs. Experiments showed that the least squares support vector machine (LS-SVM) algorithm was more accurate in predicting a services list and a negotiation mechanism using AM decreased communication time effectively, which improved the success rate of service selection and reduced the execution time of service composition. (C) 2016 Elsevier B.V. All rights reserved.; Current service selection is unable to perform proactively. When a service provider overloads, the services list is ever-lengthening, which leads to backlog and failure of service composition. To compensate for this deficiency, this paper improves the proactive service selection. In this strategy, the service provider analyses a time series of services received to forecast the backlog and consign services to the others through a negotiation process. The least squares support vector learning is used to predict a random list of services, and an acquaintance model (AM) makes a consigner allocate backlog services to other providers with high degree of relationship. The backlog of services by forecasting is entrusted to the provider who can implement the same service, and negotiation between the providers with the same role would allow generation of a new service selection solution before a fault occurs. Experiments showed that the least squares support vector machine (LS-SVM) algorithm was more accurate in predicting a services list and a negotiation mechanism using AM decreased communication time effectively, which improved the success rate of service selection and reduced the execution time of service composition. (C) 2016 Elsevier B.V. All rights reserved. |
| Indexed Type | SCI |
| Keyword | Service Selection Acquaintance Model Negotiation Ls-svm |
| Department | Beijing Inst Technol, Sch Software, Beijing 100081, Peoples R China. Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China. |
| Language | 英语 |
| WOS ID | WOS:000384871700008 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/17297 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Hu, JJ,Chen, XL,Zhang, CY. Proactive service selection based on acquaintance model and LS-SVM[J]. NEUROCOMPUTING,2016,211:60-65. |
| APA | Hu, JJ,Chen, XL,&Zhang, CY.(2016).Proactive service selection based on acquaintance model and LS-SVM.NEUROCOMPUTING,211,60-65. |
| MLA | Hu, JJ,et al."Proactive service selection based on acquaintance model and LS-SVM".NEUROCOMPUTING 211(2016):60-65. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 1-s2.0-S092523121630（1132KB） | 开放获取 | License | Application Full Text | |||
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