The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
QoS Ranking Prediction for Cloud Services
Abstract
Cloud computing is becoming popular. Building high-quality cloud applications is a critical research problem. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes a QoS ranking prediction framework for cloud services by taking advantage of the past service usage experiences of other consumers. Our proposed framework requires no additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches are proposed to predict the QoS rankings directly. Comprehensive experiments are conducted employing real-world QoS data, including 300 distributed users and 500 real-world web services all over the world. The experimental results show that our approaches outperform other competing approaches.
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Cites
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Above the Clouds: A Berkeley View of Cloud Computing
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Amazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
GroupLens: an open architecture for collaborative filtering of netnews
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Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Breese, J. S., Heckerman, D. E., & Kadie, C. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.7363 · Cited by 4,772
Hybrid Recommender Systems: Survey and Experiments
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QoS-aware middleware for Web services composition
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Item-based top-N recommendation algorithms
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Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing
Iosup, A., Ostermann, S., Yigitbasi, M. N., Prodan, R., Fahringer, T., & Epema, D. H. J. (2011) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2011.66 · Cited by 867
Cited by
Scheduling of big data applications on distributed cloud based on QoS parameters
Sandhu, R., & Sood, S. K. (2015) · Cluster Computing · doi:10.1007/s10586-014-0416-6 · Cited by 54
Metadata
- Authors
- Zheng, Z., Wu, X., Zhang, Y., Lyu, M. R., & Wang, J.
- Venue
- IEEE Transactions on Parallel and Distributed Systems
- Published
- 2013-06
- Volume
- 24
- Issue
- 6
- Pages
- 1213-1222
- ISSN
- 1045-9219 (ISSN-L)
- OpenAlex
- W2109966714
- Cited by
- 309
- References
- 21
- FWCI
- 63.78
- Type
- journal article
- Language
- en
- Open access
- closed
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