Name
Abstract | ||
|---|---|---|
Big services, both virtual (e.g., cloud services) and physical (e.g., public transportation), are evolving rapidly to handle and deal with big data. By aggregating services from various domains, big services adopt selection schemes to produce composite service solutions that meet customer requirements. However, unlike traditional service selection, a huge number of big services require some lengthy selection processes to improve the service reliability. In this paper, we propose an efficient big service selection approach based on the coefficient of variation and mixed integer programming that improves the solution in two senses: 1) minimizing the time cost and 2) maximizing the reliability. We tested our approach on real-world datasets, and the experimental results indicated that our approach is superior to others. |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s10796-017-9767-x | Information Systems Frontiers |
Keywords | Field | DocType |
Service computing,Big service,Service selection,QoS | Services computing,Computer science,Quality of service,Knowledge management,Public transport,Integer programming,Service selection,Service level requirement,Big data,Database,Cloud computing,Distributed computing | Journal |
Volume | Issue | ISSN |
19 | 6 | 1387-3326 |
Citations | PageRank | References |
1 | 0.38 | 20 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huang Ling | 1 | 83 | 19.11 |
| Qinglin Zhao | 2 | 158 | 26.30 |
| Yan Li | 3 | 399 | 95.68 |
| Shangguang Wang | 4 | 816 | 88.84 |
| Lei Sun | 5 | 55 | 18.82 |
| W. Chou | 6 | 54 | 12.78 |