制造云服务组合中支持服务关联的QoS感知评估模型

谢晓兰; 曾兰英; 翟青海

doi:10.11959/j.issn.1000-436x.2021021

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制造云服务组合中支持服务关联的QoS感知评估模型

学术论文 | 更新时间：2024-06-05

- 制造云服务组合中支持服务关联的QoS感知评估模型
- QoS aware evaluation model supporting service correlation in manufacturing cloud service composition
- 通信学报 2021年42卷第1期页码：118-129
- 作者机构：
  
  1. 桂林理工大学广西嵌入式技术与智能系统重点实验室，广西桂林 541004
  2. 桂林理工大学信息科学与工程学院，广西桂林 541004
- 作者简介：
  
  [ "谢晓兰（1974- ），女，广西桂林人，博士，桂林理工大学教授、博士生导师，主要研究方向为云计算、并行计算、大数据、地球物理勘查与信息技术等。" ]
  [ "曾兰英（1995- ），女，重庆人，桂林理工大学硕士生，主要研究方向为云计算、大数据。" ]
  [ "翟青海（1997- ），男，安徽合肥人，桂林理工大学硕士生，主要研究方向为云计算、大数据。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61762031);广西科技重大专项基金资助项目(AA19046004);广西重点研发计划基金资助项目(AB18126006)
- DOI：10.11959/j.issn.1000-436x.2021021
  中图分类号： TP3-05
- 网络出版日期：2021-01，
  
  纸质出版日期：2021-01-25
- 稿件说明：
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谢晓兰, 曾兰英, 翟青海. 制造云服务组合中支持服务关联的QoS感知评估模型[J]. 通信学报, 2021,42(1):118-129.

Xiaolan XIE, Lanying ZENG, Qinghai ZHAI. QoS aware evaluation model supporting service correlation in manufacturing cloud service composition[J]. Journal on communications, 2021, 42(1): 118-129.
谢晓兰, 曾兰英, 翟青海. 制造云服务组合中支持服务关联的QoS感知评估模型[J]. 通信学报, 2021,42(1):118-129. DOI： 10.11959/j.issn.1000-436x.2021021.

Xiaolan XIE, Lanying ZENG, Qinghai ZHAI. QoS aware evaluation model supporting service correlation in manufacturing cloud service composition[J]. Journal on communications, 2021, 42(1): 118-129. DOI： 10.11959/j.issn.1000-436x.2021021.

摘要

为了更好地从大量候选云服务集中选出满足制造需求且服务质量较优的制造云服务协同完成制造任务，针对QoS准确性给组合筛选带来的影响，基于接口关联、业务实体关联、统计关联，提出了一种支持服务关联的QoS感知评估模型。引入企业合作时间、KULC和IR参数，利用历史信息进行预测对统计关联QoS变化量进行动态调整，保证了时效性。通过算例分析验证了所提方法的可行性，实验结果表明该方法能为服务需求方提供更切合实际的解决方案。

Abstract

In order to select better quality of manufacturing cloud services that meet the demand of manufacture to complete manufacturing tasks in coordination

a QoS aware evaluation model supporting service correlation was proposed based on interface correlation

business entity correlation

and statistics correlation

aiming at the impact of QoS accuracy on combination screening.The cooperation time

KULC and IR parameters were introduced

and the historical information was used to predict and dynamically adjust the statistical association QoS changes to ensure timeliness.The feasibility of the proposed method is verified by an example analysis

and the experimental results show that the method can provide a more realistic solution for the service demander.

关键词

Keywords

references

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