Proactive service selection based on acquaintance model and LS-SVM

ISCAS OpenIR

	Proactive service selection based on acquaintance model and LS-SVM
	Hu, JJ; Chen, XL; Zhang, CY
	2016
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	211 页码:60-65
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.; 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
关键词	Service Selection Acquaintance Model Negotiation Ls-svm
部门归属	Beijing Inst Technol, Sch Software, Beijing 100081, Peoples R China. Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China.
语种	英语
WOS记录号	WOS:000384871700008
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/17297
专题	中国科学院软件研究所
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.

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