Name
Abstract | ||
|---|---|---|
In this paper, we propose a deep learning approach for long-term Quality of Service (QoS)-based service composition. Existing techniques for quality-aware service composition mostly focus on static QoS values observed during composition time. They do not consider potential QoS fluctuations in the long run when selecting services for composition or substitution. Our approach uses deep recurrent Long Short Term Memories (LSTMs) to forecast future QoS. The predicted QoS values are used to accurately recommend components and substitutes in long-term service compositions. Experiments show promising results compared to existing QoS prediction techniques. |
Year | Venue | Field |
|---|---|---|
2017 | ICSOC | Data mining,Computer science,Quality of service,Service composition,Artificial intelligence,Deep learning,Distributed computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamza Labbaci | 1 | 0 | 0.34 |
| Brahim Medjahed | 2 | 1077 | 73.34 |
| Youcef Aklouf | 3 | 0 | 0.34 |