基于特征深度融合的Web服务QoS联合预测

doi:10.11959/j.issn.1000-436x.2022107

Abstract

Abstract:

In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed with considering of the hidden environmental preference information in QoS and the common features of multi-class QoS.First, QoS data was modeled as a user-service bipartite graph and multi-component graph convolution neural network was used for feature extraction and mapping, and the weighted fusion method was used for the same dimensional mapping of multi-class of QoS features.Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features, and high-order interactive features of the mapped feature vector.Finally, the results of each part were combined to achieve the joint QoS prediction.The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE).

Key words: joint prediction, quality of service, preference feature, deep fusion

CLC Number:

TN92

Jianxun LIU, Linghang DING, Guosheng KANG, Buqing CAO, Yong XIAO. Joint QoS prediction for Web services based on deep fusion of features[J]. Journal on Communications, 2022, 43(7): 215-226.

Figures/Tables 9

方法	DoT=5%		DoT=10%		DoT=15%		DoT=20%		DoT=25%		DoT=30%
方法	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE
UIPCC	0.197 1	0.086 1	0.184 9	0.079 8	0.177 4	0.073 2	0.166 1	0.068 9	0.158 1	0.066 5	0.152 8	0.058 6
PMF	0.154 8	0.069 7	0.152 4	0.066 7	0.147 5	0.064 8	0.142 6	0.061 9	0.134 1	0.058 5	0.129 3	0.052 1
DNM	0.142 8	0.061 9	0.139 6	0.060 5	0.136 7	0.059 3	0.130 8	0.056 9	0.129 5	0.052 2	0.123 5	0.051 2
MLP-ANFM	0.142 2	0.061 1	0.139 3	0.059 4	0.135 8	0.059 1	0.128 0	0.054 4	0.127 5	0.050 2	0.122 0	0.050 3
MGCN-MLP	0.141 4	0.060 4	0.138 7	0.058 5	0.134 6	0.057 8	0.128 5	0.053 8	0.124 7	0.049 9	0.120 8	0.488 0
Single-MGCN	0.140 8	0.059 9	0.138 8	0.057 7	0.134 4	0.057 2	0.128 2	0.056 5	0.123 0	0.0500	0.118 6	0.047 9
JQSP	$0 . 1357$	$0 . 0587$	$0 . 1335$	$0 . 0554$	$0 . 1296$	$0 . 0525$	$0 . 1220$	$0 . 0504$	$0 . 1126$	$0 . 0477$	$0 . 0988$	$0 . 0452$
Gains	4.97%	5.17%	4.37%	8.43%	5.19%	11.47%	6.73%	11.42%	13.05%	8.62%	20.00%	11.72%

方法	DoT=5%		DoT=10%		DoT=15%		DoT=20%		DoT=25%		DoT=30%
方法	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE
UIPCC	0.216 2	0.117 8	0.200 5	0.101 6	0.198 4	0.096 9	0.190 3	0.088 3	0.179 2	0.084 7	0.164 1	0.076 4
PMF	0.180 2	0.086	0.176 4	0.075 6	0.168 5	0.069 2	0.160 2	0.061 6	0.158 5	0.059 4	0.154 3	0.058 8
DNM	0.184 4	0.082 2	0.179 8	0.075 8	0.170 9	0.064 3	0.162 9	0.060 5	0.155 7	0.054 7	0.151 0	0.052 9
MLP-ANFM	0.182 3	0.083 6	0.177 0	0.073 8	0.168 8	0.066 3	0.161 1	0.060 0	0.154 8	0.053 8	0.147 7	0.051 1
MGCN-MLP	0.180 3	0.080 5	0.177 2	0.071 6	0.165 8	0.064 2	0.160 3	0.059 9	0.150 2	0.053 8	0.142 8	0.051 5
Single-MGCN	0.179 8	0.080 4	0.175 9	0.072 8	0.162 5	0.061 7	0.158 9	0.059 2	0.148 8	0.052 9	0.137 9	0.051 6
JQSP	$0 . 1725$	$0 . 0788$	$0 . 1659$	$0 . 0701$	$0 . 1524$	$0 . 0602$	$0 . 1465$	$0 . 0575$	$0 . 1333$	$0 . 0510$	$0 . 1073$	$0 . 0502$
Gains	6.45%	4.14%	7.73%	7.52%	10.83%	6.38%	10.07%	4.96%	14.39%	6.76%	28.96%	5.10%

References 30

[1]	方晨, 张恒巍, 张铭 ,等. 基于信任扩展和列表级排序学习的服务推荐方法[J]. 通信学报, 2018,39(1): 147-158.
	FANG C , ZHANG H W , ZHANG M ,et al. Trust expansion and listwise learning-to-rank based service recommendation method[J]. Journal on Communications, 2018,39(1): 147-158.
[2]	赵晨阳, 王俊岭 . 基于隐含上下文支持向量机的服务推荐方法[J]. 通信学报, 2019,40(9): 61-73.
	ZHAO C Y , WANG J L . Service recommendation method based on context-embedded support vector machine[J]. Journal on Communications, 2019,40(9): 61-73.
[3]	YIN Y Y , CHEN L , XU Y S ,et al. QoS prediction for service recommendation with deep feature learning in edge computing environment[J]. Mobile Networks and Applications, 2020,25(2): 391-401.
[4]	WANG X , WANG R J , SHI C ,et al. Multi-component graph convolutional collaborative filtering[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(4): 6267-6274.
[5]	DING L , KANG G , LIU J ,et al. QoS prediction for Web services via combining multi-component graph convolutional collaborative filtering and deep factorization machine[C]// IEEE International Conference on Web Services. Piscataway:IEEE Press, 202: 551-559.
[6]	SHAO L S , ZHANG J , WEI Y ,et al. Personalized QoS prediction forWeb services via collaborative filtering[C]// Proceedings of IEEE International Conference on Web Services. Piscataway:IEEE Press, 2007: 439-446.
[7]	CHEN L , FENG Y P , WU J ,et al. An enhanced QoS prediction approach for service selection[C]// Proceedings of 2011 IEEE International Conference on Services Computing. Piscataway:IEEE Press, 2011: 727-728.
[8]	任丽芳, 王文剑 . 一种移动边缘计算环境中服务QoS的预测方法[J]. 小型微型计算机系统, 2020,41(6): 1176-1181.
	REN L F , WANG W J . Method for QoS prediction in mobile edge computing environment[J]. Journal of Chinese Computer Systems, 2020,41(6): 1176-1181.
[9]	WANG S G , ZHAO Y L , HUANG L ,et al. QoS prediction for service recommendations in mobile edge computing[J]. Journal of Parallel and Distributed Computing, 2019,127: 134-144.
[10]	邓璇, 吕晟凯 . 基于信誉感知与嵌入式学习的Web服务QoS预测研究[J]. 物联网技术, 2021,11(12): 99-103.
	DENG X , LYU S K . Research on Web service QoS prediction based on reputation perception and embedded learning[J]. Internet of Things Technologies, 2021,11(12): 99-103.
[11]	SALAKHUTDINOV R , MNIH A , HINTON G . Restricted Boltzmann machines for collaborative filtering[C]// Proceedings of the 24th International Conference on Machine Learning. New York:ACM Press, 2007: 791-798.
[12]	LUO X , ZHOU M C , WANG Z D ,et al. An effective scheme for QoS estimation via alternating direction method-based matrix factorization[J]. IEEE Transactions on Services Computing, 2019,12(4): 503-518.
[13]	鲁城华, 寇纪淞 . 基于用户和服务区域信息的个性化 Web 服务质量预测[J]. 管理科学, 2020,33(2): 63-75.
	LU C H , KOU J S . Personalized QoS prediction for Web services based on the region information of users and services[J]. Journal of Management Science, 2020,33(2): 63-75.
[14]	CHEN L , XIE F F , ZHENG Z B ,et al. Predicting quality of service via leveraging location information[J]. Complexity,2019, 2019:4932030.
[15]	TANG M D , LIANG W , YANG Y T ,et al. A factorization machine-based QoS prediction approach for mobile service selection[J]. IEEE Access, 2019,7: 32961-32970.
[16]	夏会, 高旻, 邹淑 . 时空感知下基于结构相似度的Web服务质量预测[J]. 重庆大学学报, 2021,44(1): 88-96.
	XIA H , GAO M , ZOU S . A structure similarity based quality prediction approach for Web service in the spatial-temporal scenario[J]. Journal of Chongqing University, 2021,44(1): 88-96.
[17]	陈蕾, 杨庚, 陈正宇 ,等. 基于结构化噪声矩阵补全的 Web 服务QoS预测[J]. 通信学报, 2015,36(6): 53-63.
	CHEN L , YANG G , CHEN Z Y ,et al. Web services QoS prediction via matrix completion with structural noise[J]. Journal on Communications, 2015,36(6): 53-63.
[18]	LUO X , WU H , YUAN H Q ,et al. Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors[J]. IEEE Transactions on Cybernetics, 2020,50(5): 1798-1809.
[19]	KANG G S , LIU J X , XIAO Y ,et al. Neural and attentional factorization machine-based Web API recommendation for mashup development[J]. IEEE Transactions on Network and Service Management, 2021,18(4): 4183-4196.
[20]	GAO H H , XU Y S , YIN Y Y ,et al. Context-aware QoS prediction with neural collaborative filtering for Internet-of-things services[J]. IEEE Internet of Things Journal, 2020,7(5): 4532-4542.
[21]	王安迪 . 基于QoS的Web服务质量预测方法研究[D]. 北京:华北电力大学(北京), 2020.
	WANG A D . Research on prediction method of web services quality based on QoS[D]. Beijing:North China Electric Power University, 2020.
[22]	CHEN D , GAO M , LIU A ,et al. A recurrent neural network based approach for Web service QoS prediction[C]// Proceedings of 2019 2nd International Conference on Artificial Intelligence and Big Data(ICAIBD). Piscataway:IEEE Press, 2019: 350-357.
[23]	ELIF A , CANBERK B . Forecasting quality of service for next- generation data-driven WiFi6 campus networks[J]. IEEE Transactions on Network and Service Management, 2021,18(4): 4744-4755.
[24]	ZHANG X W , LI L . Attentional neural factorization machines for knowledge tracing[C]// Knowledge Science,Engineering and Management. Berlin:Springer, 2021: 319-330.
[25]	RENDLE S . Factorization machines with libFM[J]. ACM Transactions on Intelligent Systems and Technology, 2012,3(3): 1-22.
[26]	VASWANI A , SHAZEER N , PARMAR N ,et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Massachusetts:MIT Press, 2017: 6000-6010.
[27]	ZHENG Z , MA H , LYU M R ,et al. QoS-aware Web service recommendation by collaborative filtering[J]. IEEE Transactions on services computing, 2011,4(2): 140-52.
[28]	ZHENG Z B , MA H , LYU M R ,et al. Collaborative Web service QoS prediction via neighborhood integrated matrix factorization[J]. IEEE Transactions on Services Computing, 2013,6(3): 289-299.
[29]	WU H , ZHANG Z X , LUO J C ,et al. Multiple attributes QoS prediction via deep neural model with contexts[J]. IEEE Transactions on Services Computing, 2021,14(4): 1084-1096.
[30]	LOUIZOS C , WELLING M , KINGMA D P . Learning sparse neural networks through L₀regularization[J]. arXiv Preprint,arXiv:171201312, 2017.

Metrics

Recommended 0

No Suggested Reading articles found!

参数	值
转换矩阵数量	3
优化器	Adam
神经网络嵌入维度	64
自注意力嵌入维度	64
Dropout	0.5
正则化系数	3.3×10^-5
激活函数	ReLU
神经网络层	2
学习率	0.0001

Joint QoS prediction for Web services based on deep fusion of features

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 30

Related Articles 15

Metrics

Recommended 0

[1]	Yuancheng LI, Yongtai QIN. Deep reinforcement learning based algorithm for real-time QoS optimization of software-defined security middle platform [J]. Journal on Communications, 2023, 44(5): 181-192.
[2]	Damin ZHANG, Yi WANG, Chengcheng ZOU, Peiwen ZHAO, Linna ZHANG. Resource allocation strategies for improved mayfly algorithm in cognitive heterogeneous cellular network [J]. Journal on Communications, 2022, 43(6): 156-167.
[3]	Gang LI,Zhijun WU. Task scheduling algorithm for system-wide information management based on multiple QoS constraints [J]. Journal on Communications, 2019, 40(7): 27-37.
[4]	Julong LAN,Xueshuai ZHANG,Yuxiang HU,Penghao SUN. Software-defined networking QoS optimization based on deep reinforcement learning [J]. Journal on Communications, 2019, 40(12): 60-67.
[5]	Weiwei XIA,Zhaoming DING,Lianfeng SHEN. Joint admission control algorithm based on load transfer in heterogeneous networks [J]. Journal on Communications, 2018, 39(5): 34-47.
[6]	Zhuo-xiong PEI,Xing-hua LI,Hai LIU,Kai-yue LEI,Jian-feng MA,Hui LI. Anonymizing region construction scheme based on query range in location-based service privacy protection [J]. Journal on Communications, 2017, 38(9): 125-132.
[7]	Xi-yang LI,Ping-zhi1 FAN. Design and analysis of multi-channel MAC scheduling code for mobile ad hoc network [J]. Journal on Communications, 2014, 35(5): 57-64.
[8]	. Design and analysis of multi-channel MAC scheduling code for mobile ad hoc network [J]. Journal on Communications, 2014, 35(5): 8-64.
[9]	Fei-long ZHAO. Low-complexity and QoS-awared scheduling algorithm for LTE [J]. Journal on Communications, 2013, 34(12): 178-184.
[10]	Yan-jing SUN,Wei FANG,Wei-dong LIU,Chong MENG. Differentiated service and ant colony optimization based routing protocol for wireless multimedia sensor networks [J]. Journal on Communications, 2012, 33(Z2): 9-14.
[11]	Lei WU,Yu-long LIU,Shi-jun LIU. Multi-strategy QoS-aware service selection and ranking model [J]. Journal on Communications, 2012, 33(Z1): 141-147.
[12]	Yun-yong ZHANG,Su-fen LI,Jun WU,Bing-yi FANG. Research on the cloud services provider-oriented services selection method [J]. Journal on Communications, 2012, 33(9): 66-76.
[13]	Juan ZHAO,Ping GUO,Hong-zhong DENG,Jun WU,Yue-jin TAN,Jian-ping LI. Modeling and analysis of performance reliability for communication networks based on traffic dynamics [J]. Journal on Communications, 2011, 32(8): 159-164.
[14]	Jun-zhou LUO,Jia-hui JIN,Ai-bo SONG,Fang DONG. Cloud computing:architecture and key technologies [J]. Journal on Communications, 2011, 32(7): 3-21.
[15]	Chang-song DING,Zhi-gang HU,Peng XIAO. Reliability enhenced resource reservation strategy in computing grid [J]. Journal on Communications, 2011, 32(7): 40-46.