QoS-aware Optimization of Cloud Service Composition using Symbiotic Organisms Search Algorithm
Subject Areas : Renewable energyVahideh Hayyolalam 1 , Ali Asghar Pourhaji Kazem 2
1 - MSc - Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 - Assistant Professor - Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Keywords: cloud computing, Qos, Service composition, SOA, Symbiotic Organism Search,
Abstract :
Nowadays, service-oriented systems according to possibility of using in heterogeneous distributed environments and being independent of the specific technology, also with the existence of large-scale dynamic system with changeable requirements, are highly regarded. Cloud services are one of the implementation methods of service-oriented concepts. Increasing the tendency of users to use cloud computing, encourages service vendors to provide services with different non-functionality features. Mostly single services couldn’t satisfy users’ requirements, so it’s necessary to compose some services to achieve the demand service. With the increasing of service providers, also services in the internet service pools, selecting the optimal service from a set of functionality equivalent candidates which are differ in QoS, becomes an important NP-Hard research problem. Therefore increasing the quality of composite services is a vital challenge and since the quality of cloud composite services derived from previous approaches can still be increased, in this research, we have tried to increase the quality of cloud composite services using the Symbiotic Organism Search Algorithm. Simulations are conducted in Matlab environment and the results are compared to three famous algorithms including GA, ACO and PSO. The comparisons demonstrate the remarkable superiority of SOS in result's quality, stability and scalability, also 13% improvement.
[1] Wikipedia, “Cloud computing - Wikipedia, The Free Encyclopedia”, 2016.
[2] A. Jula, H. Nilsaz, E. Sundararajan, Z. Othman, “A new dataset and benchmark for cloud computing service composition”, Proceeding of the IEEE/ISMS, pp. 83–86, Langkawi, Malaysia, Jan. 2014.
[3] A. Jula, Z. Othman, E. Sundararajan, “Imperialist competitive algorithm with PROCLUS classifier for service time optimization in cloud computing service composition”, Expert Systems with Applications, Vol. 42, No. 1, pp. 135–145, Jan. 2015.
[4] A. Jula, E. Sundararajan, Z. Othman, “Cloud computing service composition: A systematic literature review”, Expert Systems with Applications, Vol. 41, No. 8, pp. 3809–3824, Jun. 2014.
[5] G. Zou, Y. Chen, Y. Yang, R. Huang, Y. Xu, “AI planning and combinatorial optimization for web service composition in cloud computing”, Proccedding of the International Conference on Cloud Computing and Virtualization, pp. 1–8, 2010.
[6] Q. Yu, L. Chen, B. Li, “Ant colony optimization applied to web service compositions in cloud computing”, Computers & Electrical Engineering, Vol. 41, pp. 18–27, 2015.
[7] H. Kurdi, A. Al-Anazi, C. Campbell, A. Al Faries, “A combinatorial optimization algorithm for multiple cloud service composition”, Computers & Electrical Engineering, Vol. 42, pp. 107–113, 2015.
[8] N.H. Rostami, E. Kheirkhah, M. Jalali, “An optimized semantic web service composition method based on clustering and ant colony algorithm”, ArXiv Prepr. ArXiv1402.2271, 2014.
[9] S. Wang, Q. Sun, H. Zou, and F. Yang, “Particle swarm optimization with skyline operator for fast cloud-based web service composition”, Mobile Networks and Applications, Vol. 18, No. 1, pp. 116–121, Feb. 2013.
[10] Z.-Z. Liu, D.-H. Chu, C. Song, X. Xue, B.-Y. Lu, “Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition”, Information Sciences, Vol. 326, pp. 315–333, 2016.
[11] Q. Wu, Q. Zhu, “Transactional and QoS-aware dynamic service composition based on ant colony optimization”, Future Generation Computer Systems, Vol. 29, No. 5, pp. 1112–1119, 2013.
[12] D. Wang, Y. Yang, Z. Mi, “A genetic-based approach to web service composition in geo-distributed cloud environment”, Computers & Electrical Engineering, Vol. 43, pp. 129-141, April 2015.
[13] S.A. Ludwig, “Applying particle swarm optimization to quality-of-service-driven web service composition”, Proceeding of the IEEE/AINA, pp. 613–620, Fukuoka, Japan, March 2012.
[14] A. Younes, M. Essaaidi, A. El Moussaoui, “SFL algorithm for QoS-based cloud service composition”, International Journal Computer Appllications, Vol. 97, No. 17, pp. 42–49, 2014.
[15] M.-Y. Cheng, D. Prayogo, “Symbiotic organisms search: A new metaheuristic optimization algorithm”, Computers and Structures, Vol. 139, pp. 98–112, July 2014.
[16] E. Al-Masri, Q.H. Mahmoud, “Discovering the best web service”, Proceedings of the IEEE/ICSMC, pp. 4250-4255, San Antonio, TX, USA , 2009.
[17] G. Canfora, M. Di Penta, R. Esposito, M.L. Villani, “An approach for QoS-aware service composition based on genetic algorithms”, Proceedings of the GECCO, pp. 1069–1075, 2005.
[18] G. Spezzano, “Using service clustering and self-adaptive MOPSO-CD for QoS-aware cloud service selection”, Procedia Computer Science, Vol. 83, pp. 512–519, 2016.
[19] L. Zaki, A. Pourhajikazem, S. Lotfi, “Providing an algorithm for QoS-aware grid service composition using ant colony algorithm”, in First National Conference on New Approaches in Computer Engineering and Data Recovery, Rudsar, Islamic Azad University, Ruddersar and Amlash, 1392.
[20] Y. Zhu, R. Y. Shtykh, Q. Jin, “A human-centric framework for context-aware flowable services in cloud computing environments”, Information Sciences, Vol. 257, pp. 231–247, Feb. 2014.
[21] J. Yu, Q.Z. Sheng, Y. Han, “Introduction to special issue on cloud and service computing”, Service Oriented Computing and Applications, Vol. 7, No. 2, pp. 75–76, June 2013.
[22] M.P. Papazoglou, “Service-oriented computing: Concepts, characteristics and directions”, Proceeding of the IEEE/WISE, pp. 3–12, Rome, Italy, Dec. 2003.
_||_