A Solution Space Reduction Approach based on Neural Network and Clustering for Large-scale Service Composition
Authors: 
Feifei Xu, Cheng ZhangAuthors Info & Claims
Pages 443 - 447
Abstract
Service composition is an important way to generate value-added services in cloud computing. The selection of optimal composition scheme for QoS-aware service composition has become a research hot topic. In a dynamic cloud service environment, the complex service composition process structure and numerous candidate services generate a huge number of service composition results. It is challenging to select best or near best service composition result that meets the user's preference in a large number of solution spaces. We propose a service composition method based on neural network model prediction and clustering to ensure the optimality of service combination results based on user preferences while further reducing the service composition algorithm search time. We carry out comparative experiments of different approaches to validate the superiority of our approach in the background of large-scale service composition.
References
[1]
Wang, H., Hu, X., Yu, Q., Gu, M., Zhao, W., Yan, J., & Hong, T. (2020). Integrating reinforcement learning and skyline computing for adaptive service composition. Information Sciences, 519, 141-160. https://doi.org/10.1016/j.ins.2020.01.039.
[2]
L. Purohit and S. Kumar, "Clustering Based Approach for Web Service Selection Using Skyline Computations," 2019 IEEE International Conference on Web Services (ICWS), Milan, Italy, 2019, pp. 260-264.
[3]
.H. Wang, X. Zhou, X. Zhou, W. Liu and W. Li, "Adaptive and Dynamic Service Composition Using Q-Learning," 2010 22nd IEEE International Conference on Tools with Artificial Intelligence, Arras, France, 2010, pp. 145-152.
[4]
Hu, Y., Wu, F., Yang, Y., & Liu, Y. (2022). Tackling temporal-dynamic service composition in cloud manufacturing systems: A tensor factorization-based two-stage approach. Journal of Manufacturing Systems, 63, 593-608. https://doi.org/10.1016/j.jmsy.2022.05.008.
[5]
Wang, H., Peng, S., & Yu, Q. (2019). A parallel refined probabilistic approach for QoS-aware service composition. Future Generation Computer Systems, 98, 609-626. https://doi.org/10.1016/j.future.2019.03.053.
[6]
Hongbign Wang, Xin Chen, Qin Wu, Qi Yu, Xingguo Hu, Zibin Zheng, and Athman Bouguettaya. 2017. Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition. ACM Trans. Auton. Adapt. Syst. 12, 2, Article 8 (June 2017), 42 pages. https://doi.org/10.1145/3058592.
[7]
Liang, H., Wen, X., Liu, Y., Zhang, H., Zhang, L., & Wang, L. (2021). Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning. Robotics and Computer-Integrated Manufacturing, 67, 101991. https://doi.org/10.1016/j.rcim.2020.101991.
[8]
Hussain, A., Chun, J., & Khan, M. (2020). A novel customer-centric Methodology for Optimal Service Selection (MOSS) in a cloud environment. Future Generation Computer Systems, 105, 562-580. https://doi.org/10.1016/j.future.2019.12.024.
[9]
S. Li, J. Huang, B. Cheng, L. Cui and Y. Shi, "FASS: A Fairness-Aware Approach for Concurrent Service Selection with Constraints," 2019 IEEE International Conference on Web Services (ICWS), Milan, Italy, 2019, pp. 255-259.
Index Terms
- A Solution Space Reduction Approach based on Neural Network and Clustering for Large-scale Service Composition
Recommendations
A clustering network-based approach to service composition in cloud manufacturing
Cloud manufacturing CMfg is a new business paradigm that aims to provide manufacturing resources as services over the Internet in a convenient pay-as-you-go mode. Service composition is a critical means for achieving value adding and synergy of ...
Large-Scale Service Composition in Semantic Service Discovery
CEC-EEE '06: Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-ServicesSelf-Healing and self-optimizing service based applications are important steps towards the self-organizing Service Oriented Architectures (SOA). Self-Organizing SOAs replace services by functional equivalent services in the case of faults or in respect ...
Adaptable service composition for very-large-scale Internet of Things systems
PDT '11: Proceedings of the Workshop on Posters and Demos TrackThe (future) Internet of Things (IoT), with service composition point of view, raises additional challenges especially with respect to handling the scale, dynamicity and heterogeneity of the target networking environment. Therefore, the services offered ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 2023
467 pages
ISBN:9798400716966
DOI:10.1145/3661638
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 01 June 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
AISNS 2023
AISNS 2023: 2023 International Conference on Artificial Intelligence, Systems and Network Security
December 22 - 24, 2023
Mianyang, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 16Total Downloads
- Downloads (Last 12 months)16
- Downloads (Last 6 weeks)3
Reflects downloads up to 02 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format