Location-Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests

Web service composition is widely used to extend the function of web services. Different users have different requirements of QoSs (Quality of Services) making them face many problems. The requirement of a special QoS may be a hard requirement or a soft requirement. The hard requirement refers to th...

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Main Authors: Junwen Lu, Guanfeng Liu, Keshou Wu, Wenjiang Qin
Format: Article
Language:English
Published: Hindawi-Wiley 2019-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2019/9871971
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spelling doaj-f2a915ec37cd48dc986a82592177af062020-11-24T22:16:18ZengHindawi-WileyComplexity1076-27871099-05262019-01-01201910.1155/2019/98719719871971Location-Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service RequestsJunwen Lu0Guanfeng Liu1Keshou Wu2Wenjiang Qin3Engineering Research Center for Software Testing and Evaluation of Fujian Province, Xiamen University of Technology, Xiamen, ChinaDepartment of Computing, Macquarie University, Sydney, NSW, AustraliaEngineering Research Center for Software Testing and Evaluation of Fujian Province, Xiamen University of Technology, Xiamen, ChinaPetro China Northwest Sales Company, ChinaWeb service composition is widely used to extend the function of web services. Different users have different requirements of QoSs (Quality of Services) making them face many problems. The requirement of a special QoS may be a hard requirement or a soft requirement. The hard requirement refers to the QoS which must be satisfied to the user, and the soft one means that the requirement is flexible. This paper tries to solve the service composition problem when there are two kinds of requirements of QoSs. To satisfy various kinds of requirement of the QoS, we propose a composition method based on our proposed framework. We give an analysis from composition models of services and from related QoE (Quality of Experience) of web services. Then, we rank the service candidates and the service requests together. Based on the ranking, a heuristics is proposed for service selection and composition-GLLB (global largest number of service requests first, local best fit service candidate first), which uses “lost value” in the scheduling to denote the QoE. Comparisons are used to evaluate our method. Comparisons show that GLLB reduces the value of NUR (Number of Unfinished service Requests), FV (Failure Value), and AFV (Average Failure Value).http://dx.doi.org/10.1155/2019/9871971
collection DOAJ
language English
format Article
sources DOAJ
author Junwen Lu
Guanfeng Liu
Keshou Wu
Wenjiang Qin
spellingShingle Junwen Lu
Guanfeng Liu
Keshou Wu
Wenjiang Qin
Location-Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests
Complexity
author_facet Junwen Lu
Guanfeng Liu
Keshou Wu
Wenjiang Qin
author_sort Junwen Lu
title Location-Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests
title_short Location-Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests
title_full Location-Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests
title_fullStr Location-Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests
title_full_unstemmed Location-Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests
title_sort location-aware web service composition based on the mixture rank of web services and web service requests
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2019-01-01
description Web service composition is widely used to extend the function of web services. Different users have different requirements of QoSs (Quality of Services) making them face many problems. The requirement of a special QoS may be a hard requirement or a soft requirement. The hard requirement refers to the QoS which must be satisfied to the user, and the soft one means that the requirement is flexible. This paper tries to solve the service composition problem when there are two kinds of requirements of QoSs. To satisfy various kinds of requirement of the QoS, we propose a composition method based on our proposed framework. We give an analysis from composition models of services and from related QoE (Quality of Experience) of web services. Then, we rank the service candidates and the service requests together. Based on the ranking, a heuristics is proposed for service selection and composition-GLLB (global largest number of service requests first, local best fit service candidate first), which uses “lost value” in the scheduling to denote the QoE. Comparisons are used to evaluate our method. Comparisons show that GLLB reduces the value of NUR (Number of Unfinished service Requests), FV (Failure Value), and AFV (Average Failure Value).
url http://dx.doi.org/10.1155/2019/9871971
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AT guanfengliu locationawarewebservicecompositionbasedonthemixturerankofwebservicesandwebservicerequests
AT keshouwu locationawarewebservicecompositionbasedonthemixturerankofwebservicesandwebservicerequests
AT wenjiangqin locationawarewebservicecompositionbasedonthemixturerankofwebservicesandwebservicerequests
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