Research Article
An Improved Artificial Bee Colony Algorithm for Cloud Computing Service Composition
@INPROCEEDINGS{10.4108/eai.19-8-2015.2260856, author={Bin Xu and Jin Qi and Kun Wang and Ye Wang and Xiaoxuan Hu and Yanfei Sun}, title={An Improved Artificial Bee Colony Algorithm for Cloud Computing Service Composition}, proceedings={11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness}, publisher={IEEE}, proceedings_a={QSHINE}, year={2015}, month={9}, keywords={service composition quality of experience artificial bee colony}, doi={10.4108/eai.19-8-2015.2260856} }- Bin Xu
Jin Qi
Kun Wang
Ye Wang
Xiaoxuan Hu
Yanfei Sun
Year: 2015
An Improved Artificial Bee Colony Algorithm for Cloud Computing Service Composition
QSHINE
IEEE
DOI: 10.4108/eai.19-8-2015.2260856
Abstract
The rapid increase of using cloud computing encourages service vendors to supply services with different features and provide them in a service pool. Service composition (SC) problem in cloud computing environment becomes a key issue because of the increase of service quantity and user requirements of the quality of service experience. To satisfy the demands on quality of service experience and realize an efficient algorithm for SC problem, a quality of experience (QoE) evaluation model based on fuzzy analytic hierarchy process (FAHP) for SC problem is put forward first. Then, an improved artificial bee colony (IABC) optimization algorithm for QoE based SC problem is proposed. The algorithm improves the updating mechanism of scout bees by introducing current global optimal solution to accelerate convergence velocity and eventually to improve the solution quality. Finally, the experimental results on QWS dataset show that IABC has a better performance on QoE based SC problem, compared with original ABC, PSO and DE.