The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Investigating QoS of Real-World Web Services
Abstract
Quality of service (QoS) is widely employed for describing nonfunctional characteristics of web services. Although QoS of web services has been investigated intensively in the field of service computing, there is a lack of real-world web service QoS data sets for validating various QoS-based techniques and models. To investigate QoS of real-world web services and to provide reusable research data sets for future research, we conduct several large-scale evaluations on real-world web services. First, addresses of 21,358 web services are obtained from the Internet. Then, three large-scale real-world evaluations are conducted. In our evaluations, more than 30 million real-world web service invocations are conducted on web services in more than 80 countries by users from more than 30 counties. Detailed evaluation results are presented in this paper and comprehensive web service QoS data sets are publicly released online.
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Cites
QoS-aware middleware for Web services composition
Zeng, L., Benatallah, B., Ngu, A. H. H., Dumas, M., Kalagnanam, J., & Chang, H. (2004) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2004.11 · Cited by 2,757
Adaptive Service Composition in Flexible Processes
Ardagna, D., & Pernici, B. (2007) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2007.1011 · Cited by 871
QoS-Aware Web Service Recommendation by Collaborative Filtering
Zheng, Z., Ma, H., Lyu, M. R., & King, I. (2011) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2010.52 · Cited by 805
Self-citationInvestigating web services on the world wide web
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Personalized QoS Prediction forWeb Services via Collaborative Filtering
Shao, L., Zhang, J., Wei, Y., Zhao, J., Xie, B., & Mei, H. (2007) · IEEE International Conference on Web Services (ICWS 2007) · doi:10.1109/icws.2007.140 · Cited by 463
Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization
Zheng, Z., Ma, H., Lyu, M. R., & King, I. (2013) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2011.59 · Cited by 443
Self-citationDistributed QoS Evaluation for Real-World Web Services
Zheng, Z., Zhang, Y., & Lyu, M. R. (2010) · 2010 IEEE International Conference on Web Services · doi:10.1109/icws.2010.10 · Cited by 366
Self-citation(2012) · Fundamentals of Dependable Computing for Software Engineers · doi:10.1201/b11667-14 · Cited by 319
Personalized QoS-Aware Web Service Recommendation and Visualization
Chen, X., Zheng, Z., Liu, X., Huang, Z., & Sun, H. (2013) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2011.35 · Cited by 184
Self-citationWSExpress: A QoS-aware Search Engine for Web Services
Zhang, Y., Zheng, Z., & Lyu, M. R.-T. (2010) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2010.20 · Cited by 124
Self-citationCloudRank: A QoS-Driven Component Ranking Framework for Cloud Computing
Zheng, Z., Zhang, Y., & Lyu, M. R.-T. (2010) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds.2010.29 · Cited by 98
Self-citationA Quality of Service Management Framework Based on User Expectations
Deora, V., Shao, J., Gray, W. A., & Fiddian, N. J. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-24593-3_8 · Cited by 94
A Faceted Classification Based Approach to Search and Rank Web APIs
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Self-citationA Bayesian network based Qos assessment model for web services
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A QoS-aware fault tolerant middleware for dependable service composition
Zheng, Z., & Lyu, M. R.-T. (2009) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2009.5270332 · Cited by 67
Self-citationFang, C.-L., Liang, D., Lin, F., & Lin, C.-C. (2006) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2006.06.001 · Cited by 64
Cited by
Gong, W., Qi, L., & Xu, Y. (2018) · Wireless Communications and Mobile Computing · doi:10.1155/2018/3075849 · Cited by 166
Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
Ateeq, M., Ishmanov, F., Afzal, M. K., & Naeem, M. (2019) · Sensors · doi:10.3390/s19020309 · Cited by 40
Metadata
- Authors
- Zheng, Z., Zhang, Y., & Lyu, M. R.
- Venue
- IEEE Transactions on Services Computing
- Published
- 2014-01
- Volume
- 7
- Issue
- 1
- Pages
- 32-39
- ISSN
- 1939-1374 (ISSN-L)
- OpenAlex
- W2060017308
- Cited by
- 444
- References
- 17
- FWCI
- 33.20
- Type
- journal article
- Language
- en
- Open access
- closed
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