Volume 0 , Issue 1 , PP: 05-14, 2019 | Cite this article as | XML | Html | PDF | Full Length Article
MAHMOUD A. SALAM 1 * , M.M.EL-GAYAR 2
There are many challenges facing the service composition process. These challenges include, how to integrate services to satisfy global user requirements, missing or changeable values of QoS, and how to reduce the large solution space of candidate services. In this paper, we proposed a framework to address these challenges. The proposed framework consists of three phases. The Normalizer phase gives a certain range for all QoS attributes and historical user orders. During the Clustering phase, the search space is reduced. Finally, the composition process is done, and a list of candidate composite services is generated through the Service composer phase. We present the hybrid bio-inspiration technique to implement the proposed framework and prove its applicability. In addition, we introduced the MR-FPSO algorithm to implement this phase by merging PSO and FOA optimization algorithms over the MapReduce framework to handle the large scale of data in the cloud environment. Our technique is compared to different techniques, including MR-GA, MR-IDPSO, and MRPSO. The simulation results proved that our technique outperforms the other techniques.
MapReduce, FOA, PSO, large scale QoS, parallel composition, Fuzzy clustering
[1] X. Guo, S. Chen, Y. Zhang, and W. Li, “Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark,” Secur. Commun. Networks, vol. 2017, no. 1, pp. 1–8, 2017.
[2] M. Alrifai and T. Risse, “Combining global optimization with local selection for efficient QoS-aware service composition,” in Proceedings of the 18th international conference on World wide web - WWW ’09, 2009, p. 881.
[3] G. Canfora, M. Di Penta, R. Esposito, and M. L. Villani, “An approach for QoS-aware service composition based on genetic algorithms,” in Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO ’05, 2005, p. 1069.
[4] N. E. A. Khalid, A. F. A. Fadzil, and M. Manaf, “Adapting MapReduce framework for genetic algorithm with large population,” in 2013 IEEE Conference on Systems, Process & Control (ICSPC), 2013, pp. 36–41.
[5] Y. Zhang, Z. Jing, and Y. Zhang, “MR-IDPSO: A novel algorithm for large-scale dynamic service composition,” Tsinghua Sci. Technol., vol. 20, no. 6, pp. 602–612, 2015.
[6] A. W. McNabb, C. K. Monson, and K. D. Seppi, “MRPSO,” in Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO ’07, 2007, p. 177.
[7] M. A. Salam, W. M. Bahgat, E. El-daydamony, and A. Atwan, “A Novel Framework for Web Service Composition,” pp. 1–11, 2019.
[8] Z. U. Rehman, O. K. Hussain, and F. K. Hussain, “Parallel cloud service selection and ranking based on QoS history,” Int. J. Parallel Program., vol. 42, no. 5, pp. 820–852, 2014.
[9] M. S. Hossain, M. M. Hassan, M. Al Qurishi, and A. Alghamdi, “Resource Allocation for Service Composition in Cloud-based Video Surveillance Platform,” in 2012 IEEE International Conference on Multimedia and Expo Workshops, 2012, pp. 408–412.
[10] H. Jin, X. Yao, and Y. Chen, “Correlation-aware QoS modeling and manufacturing cloud service composition,” J. Intell. Manuf., vol. 28, no. 8, pp. 1947–1960, 2017.
[11] F. Seghir and A. Khababa, “A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition,” J. Intell. Manuf., vol. 29, no. 8, pp. 1773–1792, Dec. 2018.
[12] Y. Chen, J. Huang, C. Lin, and J. Hu, “A Partial Selection Methodology for Efficient QoS-Aware Service Composition,” IEEE Trans. Serv. Comput., vol. 8, no. 3, pp. 384–397, May 2015.
[13] M. S. Hossain, M. Moniruzzaman, G. Muhammad, A. Ghoneim, and A. Alamri, “Big Data-Driven Service Composition Using Parallel Clustered Particle Swarm Optimization in Mobile Environment,” IEEE Trans. Serv. Comput., vol. 9, no. 5, pp. 806–817, Sep. 2016.
[14] Z. Yong, L. Wei, L. Junzhou, and Z. Xiao, “A novel two-phase approach for QoS-aware service composition based on history records,” Proc. - 2012 5th IEEE Int. Conf. Serv. Comput. Appl. SOCA 2012, 2012.
[15] Q. Wu, Q. Zhu, and M. Zhou, “A correlation-driven optimal service selection approach for virtual enterprise establishment,” J. Intell. Manuf., vol. 25, no. 6, pp. 1441–1453, Dec. 2014.
[16] H. Jin, X. Yao, and Y. Chen, “Correlation-aware QoS modeling and manufacturing cloud service composition,” J. Intell. Manuf., vol. 28, no. 8, pp. 1947–1960, Dec. 2017.
[17] Y. Ma, S. Wang, P. C. K. Hung, C. H. Hsu, Q. Sun, and F. Yang, “A highly accurate prediction algorithm for unknown web service QoS values,” IEEE Trans. Serv. Comput., vol. 9, no. 4, pp. 511–523, 2016.
[18] H. Al-Helal and R. Gamble, “Introducing Replaceability into Web Service Composition,” IEEE Trans. Serv. Comput., vol. 7, no. 2, pp. 198–209, Apr. 2014.
[19] Z. Ye, X. Zhou, and A. Bouguettaya, “Genetic Algorithm Based QoS-Aware Service Compositions in Cloud Computing,” Springer, Berlin, Heidelberg, 2011, pp. 321–334.
[20] S. Borzsonyil and K. Stocker, “The Skyline Operator *,” pp. 421–430, 2001.
[21] K. Deng, X. Zhou, and H. Tao, “Multi-source Skyline Query Processing in Road Networks,” in 2007 IEEE 23rd International Conference on Data Engineering, 2007, pp. 796–805.
[22] A. Vlachou, C. Doulkeridis, and Y. Kotidis, “Angle-based space partitioning for efficient parallel skyline computation,” in Proceedings of the 2008 ACM SIGMOD international conference on Management of data - SIGMOD ’08, 2008, p. 227.
[23] C. Wang and W. Song, “A modified particle swarm optimization algorithm based on velocity updating mechanism,” Ain Shams Eng. J., Mar. 2019.
