Proactive service selection based on acquaintance model and LS-SVM
文献类型：期刊论文
| 作者 | Hu, JJ ; Chen, XL ; Zhang, CY |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2016 |
| 卷号 | 211页码:60-65 |
| 关键词 | Service selection Acquaintance model Negotiation LS-SVM |
| ISSN号 | 0925-2312 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000384871700008 |
| 公开日期 | 2016-12-09 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/17297]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：软件研究所
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