制造云服务组合中支持服务关联的QoS感知评估模型

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

通信学报 ›› 2021, Vol. 42 ›› Issue (1): 118-129.doi: 10.11959/j.issn.1000-436x.2021021

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¹^,², 曾兰英², 翟青海²

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

修回日期:2020-12-11 出版日期:2021-01-25 发布日期:2021-01-01
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。
基金资助:
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)

QoS aware evaluation model supporting service correlation in manufacturing cloud service composition

Xiaolan XIE¹^,², Lanying ZENG², Qinghai ZHAI²

¹ Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541004, China
² College of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China

Revised:2020-12-11 Online:2021-01-25 Published:2021-01-01
Supported by:
The National Natural Science Foundation of China(61762031);The Science and Technology Major Project of Guangxi(AA19046004);The Key Research and Development Program of Guangxi(AB18126006)

摘要/Abstract

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。

关键词: 服务关联, 服务组合, QoS感知, 制造云

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.

Key words: service correlation, service composition, QoS aware, manufacturing cloud

中图分类号:

TP3-05

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.

图/表 15

表1

符号表示与含义"

参数	含义
MCS_i	第i个制造云服务
$q_{0}^{k} ({MCS}_{i})$	第i个制造云服务的第k个QoS属性的默认值
QoS(MCS_i)	第i个制造云服务的QoS值
Time(MCS_i)	第i个制造云服务的执行时间
Cost(MCS_i)	第i个制造云服务的执行费用成本
Rep(MCS_i)	第i个制造云服务的信誉度
Rel(MCS_i)	第i个制造云服务的可靠性
Ava(MCS_i)	第i个制造云服务的可用性
MCSC	某一制造云服务组合

表1

图1

图2

图3

表2

表3

图4

图5

表4

图6

表5

服务组合加权QoS的排名变化"

组合编号	不含关联分析方法		文献[15]中含关联的QoS模型		本文所提模型
组合编号	加权QoS₁	排名	加权QoS₂	排名	加权QoS₃	排名
1	0.297	1	0.499	5	0.504	4
2	0.296	2	0.558	2	0.572	1
$3$	$0 . 271$	$3$	$0 . 455$	$8$	$0 . 499$	$5$
4	0.267	4	0.56	1	0.569	2
5	0.256	5	0.454	9	0.469	9
$6$	$0 . 25$	$6$	$0 . 531$	$3$	$0 . 491$	$6$
7	0.243	7	0.465	7	0.481	8
8	0.237	8	0.486	6	0.491	7
9	0.224	9	0.524	4	0.539	3
10	0.211	10	0.425	10	0.431	10

表5

图7

图8

表6

不含合作时间与含合作时间的排名变化"

组合编号	不含合作时间			含合作时间
组合编号	加权QoS	排名	变化	加权QoS	排名	变化
1	0.476	4	-3	0.504	4	-3
2	0.515	1	1	0.572	1	1
3	0.471	5	-2	0.499	5	-2
4	0.502	2	2	0.569	2	2
$5$	$0 . 444$	$8$	$- 3$	$0 . 469$	$9$	$- 4$
6	0.463	6	0	0.491	6	0
$7$	$0 . 456$	$7$	$0$	$0 . 481$	$8$	$- 1$
$8$	$0 . 44$	$9$	$- 1$	$0 . 491$	$6$	$2$
9	0.477	3	6	0.539	3	6
10	0.391	10	0	0.431	10	0

表6

表7

不同时间加权QoS排名前10的Pareto解集"

2020年6月1日			2020年7月1日			2020年8月1日
服务组合	加权QoS	排名	服务组合	加权QoS	排名	服务组合	加权QoS	排名
[c1_s1,c4_s3,c9_s2,c10_s1,c14_s3]	0.569	1	[c1_s1,c4_s3,c9_s2,c10_s1,c14_s3]	0.569	1	[c1_s1,c4_s3,c9_s2,c10_s1,c14_s3]	0.569	1
[c3_s1,c4_s2,c9_s2,c10_s1,c14_s3]	0.56	2	[c3_s1,c4_s2,c9_s2,c10_s1,c14_s3]	0.562	2	[c3_s1,c4_s2,c9_s2,c10_s1,c14_s3]	0.562	2
[c1_s1,c4_s3,c9_s2,c11_s2,c14_s3]	0.551	3	[c1_s1,c4_s3,c9_s2,c11_s2,c14_s3]	0.551	3	[c1_s1,c4_s3,c9_s2,c11_s2,c14_s3]	0.551	3
$[c 1_s 2, c 4_s 3, c 9_s 1, c 11_s 2, c 13_s 2]$	$0.542$	$4$	[c2_s2,c4_s3,c9_s2,c12_s1,c14_s3]	0.54	4	[c2_s2,c4_s3,c9_s2,c12_s1,c14_s3]	0.54	4
[c2_s2,c4_s3,c9_s2,c12_s1,c14_s3]	0.54	5	$[c 1_s 2, c 4_s 3, c 9_s 1, c 11_s 2, c 13_s 2]$	$0.538$	$5$	[c1_s2,c4_s3,c9_s1,c11_s2,c13_s2]	0.538	5
[c1_s2,c4_s2,c9_s1,c11_s2,c14_s3]	0.533	6	[c1_s2,c4_s2,c9_s1,c11_s2,c14_s3]	0.532	6	$[c 1_s 1, c 4_s 3, c 9_s 2, c 12_s 1, c 14_s 3]$	$0.537$	$6$
[c1_s1,c4_s3,c9_s1,c11_s2,c13_s3]	0.528	7	[c1_s1,c4_s3,c9_s1,c11_s2,c13_s3]	0.526	7	[c1_s2,c4_s2,c9_s1,c11_s2,c14_s3]	0.532	7
[c1_s2,c6_s1,c9_s1,c11_s2,c14_s3]	0.524	8	[c1_s2,c6_s1,c9_s1,c11_s2,c14_s3]	0.522	8	[c1_s1,c4_s3,c9_s1,c11_s2,c13_s3]	0.526	8
[c3_s1,c4_s2,c9_s1,c10_s1,c14_s3]	0.518	9	[c3_s1,c4_s2,c9_s1,c10_s1,c14_s3]	0.518	9	[c1_s2,c6_s1,c9_s1,c11_s2,c14_s3]	0.522	9
[c2_s2,c4_s2,c9_s1,c11_s2,c14_s3]	0.513	10	[c2_s2,c4_s2,c9_s1,c11_s2,c14_s3]	0.513	10	[c3_s1,c4_s2,c9_s1,c10_s1,c14_s3]	0.518	10

表7

参考文献 20

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子任务	Time/天	Cost/万元	Rep/Ava/Rel
MST₁	[1,3]	[5,10]	[0.4,1]
MST₂	[4,10]	[30,60]	[0.4,1]
MST₃	[5,9]	[30,50]	[0.4,1]
MST₄	[4,7]	[10,30]	[0.4,1]
MST₅	[2,5]	[15,35]	[0.4,1]

参数	制造需求
Time_max/天	140
Cost_max/万元	30
Rep_min	0.8
Ava_min	0.16
Rel_min	0.16

组合编号	MCSC
1	[c2_s2, c4_s3, c9_s2, c10_s1, c13_s2]
2	[c1_s1, c4_s3, c9_s2, c10_s1, c14_s3]
3	[c1_s3, c4_s3, c9_s2, c10_s1, c13_s2]
4	[c2_s2, c6_s1, c9_s2, c10_s1, c14_s3]
5	[c1_s2, c4_s3, c9_s1, c10_s1, c13_s2]
6	[c1_s1, c4_s3, c9_s1, c10_s1, c14_s1]
7	[c2_s2, c4_s3, c9_s1, c10_s1, c13_s2]
8	[c1_s3, c6_s1, c9_s2, c10_s1, c14_s3]
9	[c3_s1, c6_s1, c9_s2, c10_s1, c14_s3]
10	[c3_s1, c4_s3, c9_s2, c11_s3, c14_s3]

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

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参考文献 20

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