AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (4.1 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

A Context-Aware Edge-Cloud Collaboration Framework for QoS Prediction

School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
Reading Academy, Nanjing University of Information Science and Technology, Nanjing 210044, China

Yong Cheng and Weihao Cao contribute equally to this paper.

Show Author Information

Abstract

The rapid growth of online services has led to the emergence of many with similar functionalities, making it necessary to predict their non-functional attributes, namely quality of service (QoS). Traditional QoS prediction approaches require users to upload their QoS data to the cloud for centralized training, leading to high user data upload latency. With the help of edge computing, users can upload data to edge servers (ESs) adjacent to them for training, reducing the upload latency. However, shallow models like matrix factorization (MF) are still used, which cannot sufficiently extract context features, resulting in low prediction accuracy. In this paper, we propose a context-aware edge-cloud collaboration framework for QoS prediction, named CQEC. Specially, to reduce the users upload latency, a distributed model training algorithm is designed with the collaboration of ESs and cloud. Furthermore, a context-aware prediction model based on convolutional neural network (CNN) and integrating attention mechanism is proposed to improve the performance. Experiments based on real-world dataset demonstrate that CQEC outperforms the baselines.

References

[1]

S. Wang, Y. Ma, B. Cheng, F. Yang, and R. N. Chang, Multi-dimensional qos prediction for service recommendations, IEEE Trans. Serv. Comput., vol. 12, no. 1, pp. 47–57, 2016.

[2]

S. Mistry, A. Bouguettaya, H. Dong, and A. K. Qin, Metaheuristic optimization for long-term iaas service composition, IEEE Trans. Serv. Comput., vol. 11, no. 1, pp. 131–143, 2018.

[3]

X. Xu, S. Tang, L. Qi, X. Zhou, F. Dai, and W. Dou, Cnn partitioning and offloading for vehicular edge networks in web3, IEEE Comm. Mag., pp. 1–7, 2023.

[4]

J. Liu, M. Tang, Z. Zheng, X. Liu, and S. Lyu, Location-aware and personalized collaborative filtering for web service recommendation, IEEE Trans. Serv. Comput., vol. 9, no. 5, pp. 686–699, 2016.

[5]

Z. Li, X. Xu, T. Hang, H. Xiang, Y. Cui, L. Qi, and X. Zhou, A knowledge-driven anomaly detection framework for social production system, IEEE Trans. Comput. Social Syst., pp. 1–14, 2022.

[6]

C. Yang, X. Xu, X. Zhou, and L. Qi, Deep q network–driven task offloading for efficient multimedia data analysis in edge computing–assisted iov, ACM Trans. Multimed. Comput. Commun. Appl., vol. 18, no. 2s, pp. 1–24, 2022.

[7]

J. Xu, J. Lin, Y. Li, and Z. Xu, Multifed: A fast converging federated learning framework for services qos prediction via cloud–edge collaboration mechanism, Knowl-Based Syst., vol. 268, p. 110463, 2023.

[8]
J. Lin, Y. Li, Z. Xu, W. She, and J. Xu, Tsfed: A two-stage federated learning framework via cloud-edge collaboration for services qos prediction, in Proc. IEEE Int. Conf. Web Services, Virtual, 2022, pp. 58–72.
[9]

Y. Zhang, K. Wang, Q. He, F. Chen, S. Deng, Z. Zheng, and Y. Yang, Covering-based web service quality prediction via neighborhood-aware matrix factorization, IEEE Trans. Serv. Comput., vol. 14, no. 5, pp. 1333–1344, 2019.

[10]
L. Shao, J. Zhang, Y. Wei, J. Zhao, B. Xie, and H. Mei, Personalized QoS Prediction forWeb Services via Collaborative Filtering, in Proc. 14th IEEE Int. Conf. Web Services, Salt Lake City, UT, USA, 2007, pp. 439–446.
[11]

Z. Chen, L. Shen, and F. Li, Exploiting web service geographical neighborhood for collaborative qos prediction, Future Gener. Comput. Syst., vol. 68, pp. 248–259, 2017.

[12]

Z. Zheng, H. Ma, M. R. Lyu, and I. King, Qos-aware web service recommendation by collaborative filtering, IEEE Trans. Serv. Comput., vol. 4, no. 2, pp. 140–152, 2010.

[13]
G. Zou, M. Jiang, S. Niu, H. Wu, S. Pang, and Y. Gan, Qos-aware web service recommendation with reinforced collaborative filtering, in Proc. Int. Conf. Service Oriented Comput., Hangzhou, China, 2018, pp. 430–445.
[14]

X. Wu, B. Cheng, and J. Chen, Collaborative filtering service recommendation based on a novel similarity computation method, IEEE Trans. Serv. Comput., vol. 10, no. 3, pp. 352–365, 2015.

[15]
C. Yu and L. Huang, Time-aware collaborative filtering for qos-based service recommendation, in Proc. IEEE Int. Conf. Web Services, Anchorage, AK, USA, 2014, pp. 265–272.
[16]
W. Qiu, Z. Zheng, X. Wang, X. Yang, and M. R. Lyu, Reputation-aware qos value prediction of web services, in Proc. 10th IEEE Int. Conf. Serv. Comput, Santa Clara, CA, USA, 2013, pp. 41–48.
[17]

Z. Zheng, H. Ma, M. R. Lyu, and I. King, Collaborative web service qos prediction via neighborhood integrated matrix factorization, IEEE Trans. Serv. Comput., vol. 6, no. 3, pp. 289–299, 2012.

[18]

H. Wu, K. Yue, B. Li, B. Zhang, and C.-H. Hsu, Collaborative qos prediction with context-sensitive matrix factorization, Future Gener. Comput. Syst., vol. 82, pp. 669–678, 2018.

[19]
Y. Xu, J. Yin, W. Lo, and Z. Wu, Personalized location-aware qos prediction for web services using probabilistic matrix factorization, in Proc. Int. Conf. Web Inf. Syst. Eng, Nanjiang, China, 2013, pp. 229–242.
[20]
W. Zhang, H. Sun, X. Liu, and X. Guo, Temporal qos-aware web service recommendation via non-negative tensor factorization, in Proc. 23rd Int. Conf. World Wide Web, New York, NY, USA, 2014, pp. 585–596.
[21]

Y. Yang, Z. Zheng, X. Niu, M. Tang, Y. Lu, and X. Liao, A location-based factorization machine model for web service qos prediction, IEEE Trans. Serv. Comput., vol. 14, no. 5, pp. 1264–1277, 2018.

[22]

M. Tang, Z. Zheng, G. Kang, J. Liu, Y. Yang, and T. Zhang, Collaborative web service quality prediction via exploiting matrix factorization and network map, IEEE Trans. Netw. Serv. Manag., vol. 13, no. 1, pp. 126–137, 2016.

[23]

J. Xu, Z. Zheng, and M. R. Lyu, Web service personalized quality of service prediction via reputation-based matrix factorization, IEEE Trans. Reliab., vol. 65, no. 1, pp. 28–37, 2015.

[24]

J. Zhu, P. He, Z. Zheng, and M. R. Lyu, Online qos prediction for runtime service adaptation via adaptive matrix factorization, IEEE Trans. Parallel. Distrib. Syst., vol. 28, no. 10, pp. 2911–2924, 2017.

[25]

H. Wu, Z. Zhang, J. Luo, K. Yue, and C.-H. Hsu, Multiple attributes qos prediction via deep neural model with contexts, IEEE Trans. Serv. Comput., vol. 14, no. 4, pp. 1084–1096, 2018.

[26]

Z. Jia, L. Jin, Y. Zhang, C. Liu, K. Li, and Y. Yang, Location-aware web service qos prediction via deep collaborative filtering, IEEE Trans. Comput. Soc. Syst., vol. 10, no. 6, pp. 3524–3535, 2022.

[27]

Y. Zhang, C. Yin, Q. Wu, Q. He, and H. Zhu, Location-aware deep collaborative filtering for service recommendation, IEEE Trans. Syst. Man. Cybern. Syst., vol. 51, no. 6, pp. 3796–3807, 2019.

[28]

Y. Xia, D. Ding, Z. Chang, and F. Li, Joint deep networks based multisource feature learning for qos prediction, IEEE Trans. Serv. Comput., vol. 15, no. 4, pp. 2314–2327, 2021.

[29]

G. Zou, S. Wu, S. Hu, C. Cao, Y. Gan, B. Zhang, and Y. Chen, Ncrl: Neighborhood-based collaborative residual learning for adaptive qos prediction, IEEE Trans. Serv. Comput., vol. 16, no. 3, pp. 2030–2043, 2022.

[30]

J. Li, H. Wu, J. Chen, Q. He, and C.-H. Hsu, Topology-aware neural model for highly accurate qos prediction, IEEE Trans. Parallel. Distrib. Syst., vol. 33, no. 7, pp. 1538–1552, 2021.

[31]

L. Gu, M. Cui, L. Xu and X. Xu, Collaborative Offloading Method for Digital Twin Empowered Cloud Edge Computing on Internet of Vehicles, Tsinghua Science and Technology, vol. 28, no. 3, pp. 433–451, 2023.

[32]
X. He, X. Du, X. Wang, F. Tian, J. Tang, and T. Chua, Outer product-based neural collaborative filtering, in Proc. 27th Int. Joint Conf. Artif. Intell., Virtual, 2018, pp. 2227–2233.
[33]

X. Xu, H. Li, Z. Li, and X. Zhou, Safe: Synergic data filtering for federated learning in cloud-edge computing, IEEE Trans. Ind. Inform., vol. 19, no. 2, pp. 1655–1665, 2023.

[34]

Z. Li, M. Bilal, X. Xu, J. Jiang, and Y. Cui, Federated learning-based cross-enterprise recommendation with graph neural networks, IEEE Trans. Ind. Inform., vol. 19, no. 1, pp. 673–682, 2023.

[35]

X. Xu, H. Li, W. Xu, Z. Liu, L. Yao and F. Dai, Artificial intelligence for edge service optimization in Internet of Vehicles: A survey, Tsinghua Science and Technology, vol. 27, no. 2, pp. 270–287, 2022.

[36]

M. Tang, T. Zhang, J. Liu, and J. Chen, Cloud service qos prediction via exploiting collaborative filtering and location-based data smoothing, Conc. Comput. Pract. Exp., vol. 27, no. 18, pp. 5826–5839, 2015.

[37]

Y. Chen, P. Yu, Z. Zheng, J. Shen, and M. Guo, Modeling feature interactions for context-aware QoS prediction of IoT services, Future Gener. Comput. Syst., vol. 137, pp. 173–185, 2022.

[38]

X. Xu, C. Yang, M. Bilal, W. Li, and H. Wang, Computation offloading for energy and delay trade-offs with traffic flow prediction in edge computing-enabled iov, IEEE Trans. Intell. Transp. Syst., pp. 1–11, 2022.

Tsinghua Science and Technology
Pages 1201-1214
Cite this article:
Cheng Y, Cao W, Fang H, et al. A Context-Aware Edge-Cloud Collaboration Framework for QoS Prediction. Tsinghua Science and Technology, 2025, 30(3): 1201-1214. https://doi.org/10.26599/TST.2024.9010027

258

Views

71

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics

Received: 07 August 2023
Revised: 02 November 2023
Accepted: 13 January 2024
Published: 30 December 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return