Using data mining to predict and generate optimum multiple execution paths compositions
In multiple execution paths compositions, can we generate solutions that simultaneously optimize all the execution paths, while meeting global QoS constraints imposed by the clients? This paper proposes a runtime path prediction method based on data mining techniqes. The method predicts, at runtime,...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Software Engineering Competence Center (SECC) of Information Technology Industry Development Agency (ITIDA).
2014
|
| Subjects: | |
| Online Access: | http://repo.uum.edu.my/14186/ http://ijse.org.eg/issues/vol-7-no-1/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In multiple execution paths compositions, can we generate solutions that simultaneously optimize all the execution paths, while meeting global QoS constraints imposed by the clients? This paper proposes a runtime path prediction method based on data mining techniqes. The method predicts, at runtime, the execution path that will be followed during the composition’s execution based on the information provided by composition requesters, making it possible to compute the optimization by considering only the predicted path. By using our method, it is expected to generate solutions that deliver the best possible QoS ratio, at the same time, minimize the violation of the global constraints. The proposed method is evaluated in terms of its prediction accuracy and scalability. |
|---|