Name
Abstract | ||
|---|---|---|
In web service composition (WSC), quality of service (QoS)-based web services selection has been a critical research issue, and many service selection methods have been presented aiming at resolving this issue, However, most of the existing methods ignore giving a risk evaluation for critical web services in WSC where transactional requirements often require compensation cost to ensure failure atomicity . To address this issue, we present a Risk-driven selection approach by incorporating the impact of failure risk of each participating task to reduce the average losses caused by execution failures of tasks for WSC. With our method, a kind of tree called failure causing tree is built to support risk losses evaluation for each task in composition web service (CWS). Particularly, our method is more suitable to the scientific computing area where many long tasks are often involved and easily lead to a lot of losses such as computing cost, communicating cost, when some failures occur in these executing tasks. The final experiment and evaluation further demonstrate the feasibility and efficiency of our method. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/GCC.2009.68 | GCC |
Keywords | Field | DocType |
service selection method,critical research issue,compensation cost,web services selection,web service composition,composition web service,existing method,transactional web service composition,critical web service,risk-driven selection approach,execution failure,data mining,business,web service,reliability,quality of service,web services,risk analysis,transaction processing,scientific computing,history | Transaction processing,Atomicity,Risk evaluation,Web service composition,Risk analysis (business),Computer science,Quality of service,Risk analysis (engineering),Web service,Transactional leadership,Database,Distributed computing | Conference |
Citations | PageRank | References |
4 | 0.42 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hai Liu | 1 | 18 | 3.19 |
| Weimin Zhang | 2 | 72 | 21.91 |
| Kaijun Ren | 3 | 132 | 23.89 |
| Zhuxi Zhang | 4 | 7 | 1.15 |
| Cancan Liu | 5 | 10 | 1.93 |