Name
Abstract | ||
|---|---|---|
Cloud computing is the next generation computing model, which has a significant position in the field of scientific and business computing. By predicting cloud service's QoS in next period, it is helpful for end users to choose the most suitable cloud service that meets their needs. The underlying hardware/software resources of cloud architecture may have a certain influence on cloud service QoS. However, existing cloud service QoS prediction approaches do not take this influence into account. As these effects are real during the process of cloud service QoS prediction, ignoring the impact of these effects may create a big gap between the prediction results and the actual results. Therefore, in this paper interactive information is first used to describe the correlation between the hardware/software resources and the QoS attributes of the cloud service. Then, a Bayesian network model is established to predict cloud QoS. Bayesian network prediction reasoning algorithm is used to predict and reason about the future QoS values. A set of dedicated experiments is conducted to validate that our approach can accurately predict QoS of cloud service and the accuracy rate is better than state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2017.2779045 | IEEE ACCESS |
Keywords | Field | DocType |
Web service,quality of service,cloud service,Bayesian network model | Reasoning algorithm,End user,Computer science,Computer network,Quality of service,Bayesian network,Software,Cloud architecture,Cloud computing,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 2 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenrui Li | 1 | 58 | 8.98 |
| Pengcheng Zhang | 2 | 40 | 10.97 |
| Hareton Leung | 3 | 982 | 65.63 |
| Shunhui Ji | 4 | 24 | 7.14 |