research-article

Trustworthy Service Selection and Composition

Authors:

Chung-Wei Hang,

Munindar P. SinghAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 6, Issue 1

Article No.: 5, Pages 1 - 17

https://doi.org/10.1145/1921641.1921646

Published: 01 February 2011 Publication History

Abstract

We consider Service-Oriented Computing (SOC) environments. Such environments are populated with services that stand proxy for a variety of information resources. A fundamental challenge in SOC is to select and compose services, to support specified user needs directly or by providing additional services. Existing approaches for service selection either fail to capture the dynamic relationships between services or assume that the environment is fully observable. In practical situations, however, consumers are often not aware of how the services are implemented. We propose two distributed trust-aware service selection approaches: one based on Bayesian networks and the other on a beta-mixture model. We experimentally validate our approach through a simulation study. Our results show that both approaches accurately punish and reward services in terms of the qualities they offer, and further that the approaches are effective despite incomplete observations regarding the services under consideration.

Supplementary Material

Appendix.pdf (a5-hang_appendix.pdf)

The proof is given in an electronic appendix, available online in the ACM Digital Library.

Download
151.29 KB

References

[1]

Amazon.com. 2009. Amazon elastic compute cloud (Amazon EC2). http://aws.amazon.com/ec2/.

[2]

Bezdek, J. C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell, MA.

Digital Library

[3]

Bishop, C. M. 2006. Pattern Recognition and Machine Learning. Springer, New York.

Digital Library

[4]

Bouguila, N., Ziou, D., and Monga, E. 2006. Practical Bayesian estimation of a finite beta mixture through Gibbs sampling and its applications. Statist. Comput. 16, 2, 215--225.

Digital Library

[5]

Bpel. 2007. Web services business process execution language, version 2.0. http://docs.oasis-open.org/wsbpel/2.0/.

[6]

Buntine, W. L. 1994. Operations for learning with graphical models. J. Artif. Intell. Res. 2, 159--225.

Digital Library

[7]

Dempster, A. P., Laird, N. M., and Rubin, D. B. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. Series B 39, 1, 1--38.

[8]

Evans, M., Hastings, N., and Peacock, B. 2000. Statistical Distributions 3rd Ed. Wiley-Interscience, New York.

[9]

Fielitz, B. D. and Myers, B. L. 1975. Estimation of parameters in the beta distribution. Decis. Sci. 6, 1, 1--13.

[10]

Friedman, N. 1998. The Bayesian structural EM algorithm. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI). Morgan Kaufmann, San Francisco, CA, 129--138.

Digital Library

[11]

Hang, C.-W., Wang, Y., and Singh, M. P. 2008. An adaptive probabilistic trust model and its evaluation. In Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems. 1485--1488.

Digital Library

[12]

Hang, C.-W., Wang, Y., and Singh, M. P. 2009. Operators for propagating trust and their evaluation in social networks. In Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS). 1025--1032.

Digital Library

[13]

Lauritzen, S. L. 1995. The EM algorithm for graphical association models with missing data. Comput. Statist. Data Anal. 19, 2, 191--201.

Digital Library

[14]

Lin, W.-L., Lo, C.-C., Chao, K.-M., and Younas, M. 2008. Consumer-Centric QoS-aware selection of Web services. J. Comput. Syst. Sci. 74, 2, 211--231.

Digital Library

[15]

Liu, W. 2005. Trustworthy service selection and composition---Reducing the entropy of service-oriented Web. In Proceedings of the 3rd IEEE International Conference on Industrial Informatics (INDIN). IEEE Computer Society, Los Alamitos, CA, 104--109.

[16]

Maximilien, E. M. and Singh, M. P. 2004. A framework and ontology for dynamic Web services selection. IEEE Internet Comput. 8, 5, 84--93.

Digital Library

[17]

McLachlan, G. and Peel, D. 2000. Finite Mixture Models. Wiley-Interscience, New York.

[18]

Menascé, D. A. 2004. Composing Web services: A QoS view. IEEE Internet Comput. 8, 6, 88--90.

Digital Library

[19]

Milanovic, N. and Malek, M. 2004. Current solutions for Web service composition. IEEE Internet Comput. 8, 6, 51--59.

Digital Library

[20]

Nepal, S., Malik, Z., and Bouguettaya, A. 2009. Reputation propagation in composite services. In Proceedings of the 7th IEEE International Conference on Web Services (ICWS). IEEE Computer Society, 295--302.

Digital Library

[21]

Paradesi, S., Doshi, P., and Swaika, S. 2009. Integrating behavioral trust in Web service compositions. In Proceedings of the 7th IEEE International Conference on Web Services (ICWS). IEEE Computer Society, Los Alamitos, CA.

Digital Library

[22]

Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco, CA.

Digital Library

[23]

Singh, M. 1997. Learning Bayesian networks from incomplete data. In Proceedings of the 14th National Conference on Artificial Intelligence (AAAI). AAAI Press, Menlo Park, CA, 534--539.

Digital Library

[24]

Singh, M. P. and Huhns, M. N. 2005. Service-Oriented Computing: Semantics, Processes, Agents. John Wiley & Sons, Chichester, UK.

[25]

Wang, Y. and Singh, M. P. 2006. Trust representation and aggregation in a distributed agent system. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI). AAAI Press, Menlo Park, 1425--1430.

Digital Library

[26]

Wang, Y. and Singh, M. P. 2010. Evidence-Based trust: A mathematical model geared for multiagent systems. ACM Trans. Auton. Adaptive Syst. 5, 3.

Digital Library

[27]

Wu, G., Wei, J., Qiao, X., and Li, L. 2007. A Bayesian network based QoS assessment model for Web services. In Proceedings of the IEEE International Conference on Services Computing. IEEE Computer Society, Los Alamitos, CA, 498--505.

[28]

Yue, K., Liu, W., and Li, W. 2007. Towards Web services composition based on the mining and reasoning of their causal relationships. In Advances in Data and Web Management. Lecture Notes in Computer Science, vol. 4505. Springer, Berlin, 777--784.

Digital Library

[29]

Zhang, N. L. and Poole, D. 1996. Exploiting causal independence in Bayesian network inference. J. Artif. Intell. Res. 5, 301--328.

Digital Library

Cited By

Gamage DRaje R(2024)Distributed Computing Applications of Context-Aware QoS and Trust Prediction FrameworkIEEE Transactions on Services Computing10.1109/TSC.2024.3463474(1-14)Online publication date: 2024
https://doi.org/10.1109/TSC.2024.3463474
Saxena UAlam T(2022)Systematic Review on Recommendation Systems to select Trustworthy Cloud Services and ensure Data Integrity2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)10.1109/ICTACS56270.2022.9987784(134-139)Online publication date: 10-Oct-2022
https://doi.org/10.1109/ICTACS56270.2022.9987784
Li WWu JCao JChen NZhang QBuyya R(2021)Blockchain-based trust management in cloud computing systems: a taxonomy, review and future directionsJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-021-00247-510:1Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1186/s13677-021-00247-5
Show More Cited By

Index Terms

Trustworthy Service Selection and Composition
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents
2. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

Recommendations

Composition and evaluation of trustworthy web services

Web service technology seamlessly enables integration of different software to fulfil dynamic business demands in a platform-neutral fashion. By means of standard interfaces, service requesters can obtain their desired functionalities through certain ...
Trustworthy assurance of service interoperation in cloud environment

Cloud computing can be realized by service interoperation and its essence is to provide cloud services through network. The development of effective methods to assure the trustworthiness of service interoperation in cloud environment is a very important ...
Workflow composition of service level agreements for web services

Service-oriented architecture enables an environment where businesses can expose services for use by their collaborators and their peer organizations. In this dynamic environment, organizations require the use of service level agreements (SLAs) to assure ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 6, Issue 1

February 2011

127 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/1921641

Issue’s Table of Contents

Copyright © 2011 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 ACM 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 February 2011

Accepted: 01 July 2010

Revised: 01 March 2010

Received: 01 June 2009

Published in TAAS Volume 6, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Army Research Office

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

66
Total Citations
View Citations
1,067
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)1

Reflects downloads up to 09 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gamage DRaje R(2024)Distributed Computing Applications of Context-Aware QoS and Trust Prediction FrameworkIEEE Transactions on Services Computing10.1109/TSC.2024.3463474(1-14)Online publication date: 2024
https://doi.org/10.1109/TSC.2024.3463474
Saxena UAlam T(2022)Systematic Review on Recommendation Systems to select Trustworthy Cloud Services and ensure Data Integrity2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)10.1109/ICTACS56270.2022.9987784(134-139)Online publication date: 10-Oct-2022
https://doi.org/10.1109/ICTACS56270.2022.9987784
Li WWu JCao JChen NZhang QBuyya R(2021)Blockchain-based trust management in cloud computing systems: a taxonomy, review and future directionsJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-021-00247-510:1Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1186/s13677-021-00247-5
Kim DJeong DSeo Y(2021)Automated composition and execution of web-based simulation systems through knowledge designing and reasoningAdvanced Engineering Informatics10.1016/j.aei.2021.10126348(101263)Online publication date: Apr-2021
https://doi.org/10.1016/j.aei.2021.101263
Han XWang LQin J(2019)Combine availability with user preferences for efficient WSCWeb Intelligence10.3233/WEB-19040517:2(105-119)Online publication date: 8-Apr-2019
https://doi.org/10.3233/WEB-190405
Li WCao JQian SBuyya R(2019)TSLAMACM Transactions on Autonomous and Adaptive Systems10.1145/331760413:4(1-41)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3317604
Wang HWang LYu QZheng Z(2019)Learning the Evolution Regularities for BigService-Oriented Online Reliability PredictionIEEE Transactions on Services Computing10.1109/TSC.2016.263326412:3(398-411)Online publication date: 1-May-2019
https://doi.org/10.1109/TSC.2016.2633264
Wang L(2019)Architecture-Based Reliability-Sensitive Criticality Measure for Fault-Tolerance Cloud ApplicationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2019.291790030:11(2408-2421)Online publication date: 1-Nov-2019
https://doi.org/10.1109/TPDS.2019.2917900
Wang YWen JZhou WTao BWu QTao Z(2019)A Cloud Service Selection Method Based on Trust and User Preference ClusteringIEEE Access10.1109/ACCESS.2019.29341537(110279-110292)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2934153
Li WCao JHu KXu JBuyya R(2019)A Trust-based Agent Learning Model for Service Composition in Mobile Cloud Computing EnvironmentsIEEE Access10.1109/ACCESS.2019.2904081(1-1)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2904081
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents