Abstract
One of the requirements of QoS-aware service composition in cloud computing environment is that it should be executed on-the-fly. It requires a trade-off between optimality and the execution speed of service composition. In line with this purpose, many researchers used combinatorial methods in previous works to achieve optimality within the shortest possible time. However, due to the ever-increasing number of services which leads to the enlargement of the search space of the problem, previous methods do not have adequate efficiency in composing the required services within reasonable time. In this paper, genetic algorithm was used to achieve global optimization with regard to service level agreement. Moreover, service clustering was used for reducing the search space of the problem, and association rules were used for a composite service based on their histories to enhance service composition efficiency. The conducted experiments acknowledged the higher efficiency of the proposed method in comparison with similar related works.
Similar content being viewed by others
References
Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616
Liu F, Tong J, Mao J, Bohn R, Messina J, Badger L, Leaf D (2011) NIST cloud computing reference architecture. NIST Spec Publ 500:292
Garg SK, Versteeg S, Buyya R (2013) A framework for ranking of cloud computing services. Future Gener Comput Syst 29(4):1012–1023
Pressman RS (2005) Software engineering: a practitioner’s approach. Palgrave Macmillan
Chiu D, Deshpande S, Agrawal G, Li R (2009) A dynamic approach toward QoSAware service workflow composition. In: IEEE International Conference on Web Services, pp 655–662
Joshi KP, Yesha Y, Finin T (2014) Automating cloud services life cycle through semantic technologies. IEEE Trans Serv Comput 7(1):109–122. doi:10.1109/TSC.2012.41
Rosenberg F, Celikovic P, Michlmayr A, Leitner P, Dustdar S (2009) An end-to-end approach for QoS-aware service composition. In: Enterprise Distributed Object Computing Conference, 2009. EDOC’09. IEEE International. IEEE, pp 151–160
Teixeira M, Ribeiro R, Oliveira C, Massa R (2015) A quality-driven approach for resources planning in service-oriented architectures. Expert Syst Appl 42(12):5366–5379
Karim R, Ding Chen, Miri A (2013) An end-to-end QoS Mapping approach for cloud service selection. In: 9th World Congress on Services, pp 341–348
Baset Salman A (2012) Cloud SLAs: present and future. ACM SIGOPS Oper Syst Rev 46(2):57–66
Kofler K, Haq Iu, Schikuta E (2010) User-Centric, heuristic optimization of service composition in clouds. In: International Conference on Euro Parallel Processing. Springer, Berlin, Heidelberg, pp 405–417
Kofler K, Schikuta E (2009) A parallel branch and bound algorithm for workflow QoS optimization. In: IEEE International Conference on Parallel Processing, pp 478–485
Huang J, Liu Y, Duan Q (2012) Service provisioning in virtualization-based cloud computing: modeling and optimization. In: GLOBECOM, pp 1710–1715
Yong Z, Wei L, Junzhou L, Xiao Z (2012) A novel two-phase approach for QoS-aware service composition based on history records. In: 5th IEEE International Conference on Service-Oriented Computingand Applications, pp 1–8
Jula A, Sundararajan E, Othman Z (2014) Cloud computing service composition: a systematic literature review. Expert Syst Appl 41(8):3809–3824
Ye Z, Zhou X, Bouguettaya A (2011) Genetic algorithm based QoS-aware service compositions in cloud computing. In: International Conference Database Systems for Advanced Applications. Springer, Berlin, Heidelberg, pp 321–334
Moscato F, Mazzocca N, Vittorini V, Lorenzo GD, Mosca P, Magaldi M (2005) Workflow pattern analysis in web services orchestration: the BPEL4WS example. In: International Conference on HighPerformance Computing and Communications, pp 395–400
da Silva AS, Ma H, Zhang M (2016) Genetic programming for QoS-aware web service composition and selection. Soft Comput 1–17. doi:10.1007/s00500-016-2096-z
Yu Y, Ma H, Zhang M (2013) An adaptive genetic programming approach to qos-aware web services composition. In: Evolutionary computation (CEC), 2013 IEEE Congress. IEEE, pp 1740–1747
AlSedrani A, Touir A (2016) Web service composition in dynamic environment: a comparative study. Comput Sci Inf Technol 75–84. doi:10.5121/csit.2016.60508
Yu Y, Ma H, Zhang M (2014) A genetic programming approach to distributed qos-aware web service composition. In: 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 1840–1846
AllamehAmiri Mohammad, Derhami Vali, Ghasemzadeh Mohammad (2013) QoS-Based web service composition based on genetic algorithm. J AI Data Min 1(2):63–73
Liu B, Meng P (2008) Hybrid algorithm combining ant colony algorithm with genetic algorithm for continuous domain. In: Young computer scientists, 2008. ICYCS 2008. The 9th International Conference. IEEE, pp 1819–1824
Zhao Z, Hong X, Wang S (2015) A web service composition method based on merging genetic algorithm and ant colony algorithm. In: Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference. IEEE, pp 1007–1011
ZHAO C, WANG J, Qin Jie, Zhang W-Q (2014) A hybrid algorithm combining ant colony algorithm and genetic algo-rithm for dynamic web service composition. Open Cybern Syst J 8:146–154
Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inf 19(1):43–53
Zhao C-Y, Wang J-L, Qin J, Zhang W-Q (2014) A hybrid algorithm combining ant colony algorithm and genetic algorithm for dynamic web service composition. Open Cybern Syst J 8:146–154
Gao F, Curry E, Ali MI, Bhiri S, Mileo A (2014) Qos-aware complex event service composition and optimization using genetic algorithms. In: International Conference on Service-Oriented Computing. Springer, Berlin, Heidelberg, pp 386–393
Wang S, Sun Q, Zou H, Yang F (2013) Particle swarm optimization with skyline operator for fast cloud-based web service composition. Mob Netw Appl 18(1):116–121
Rostami NH, Kheirkhah E, Jalali M (2014) An optimized semantic web service composition method based on clustering and ant colony algorithm. arXiv:1402.2271
Seghir F, Khababa A (2016) A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition. J Intell Manuf 1–20. doi:10.1007/s10845-016-1215-0
Mabrouk NB, Beauche S, Kuznetsova E, Georgantas N, Issarny V (2009) QoS-aware service composition in dynamic service oriented environments. In: International Conference on Middleware. Springer, Berlin, Heidelberg, pp 123–142
Xia Y, Chen P, Bao L, Wang M, Yang J (2011) A QoS-aware web service selection algorithm based on clustering. In: IEEE International Conference on Web Services (ICWS), pp 428–435
Deng SY, Du YY (2013) Web service composition approach based on service cluster and Qos. J Comput Appl 33(8):2167–2166
Ardagna D, Pernici B (2006) Global and local QOS quarantee in web service selection. In: Business Process Management Workshops, pp 32–46
Sun SX, Zhao J (2012) A decomposition-based approach for service composition with global QoS quarantees. J Inf Sci 199(1):138–153
Alrifai M, Risse T, Nejdl W (2012) A hybrid approach for efficient web service composition with end-to-end QoS constraints. ACM Trans Web 6(2):7
Liu Z-Z, Chu D-H, Jia Z-P, Shen J-Q, Wang L (2016) Two-stage approach for reliable dynamic web service composition. Knowl Based Syst 97:123–143. doi:10.1016/j.knosys.2016.01.010
Tao F, LaiLi Y, Xu L, Zhang L (2013) FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans Ind Inf 9(4):2023–2033
Wang D, Yang Y, Mi Z (2014) A genetic-based approach to web service composition in geo- distributed cloud environment. Comput Electr Eng 43:129–141. doi:10.1016/j.compeleceng.2014.10.008
Jula A, Othman Z, Sundararajan E (2013) A hybrid imperialist competitive-gravitational attraction search algorithm to optimize cloud service composition. In: IEEE Workshop on Memetic Computing, pp 37–43
Fan X-Q (2013) A decision-making method for personalized composite service. Expert Syst Appl 40(15):5804–5810
Xu Y, Yin J, Deng S, Xiong NN, Huang J (2016) Context-aware QoS prediction for web service recommendation and selection. Expert Syst Appl 53:75–56. doi:10.1016/j.eswa.2016.01.010
Kurdi H, Al-Anazi A, Campbell C, Al Faries A (2015) A combinatorial optimization algorithm for multiple cloud service composition. Comput Electr Eng 42:107–113
Yu Q, Chen L, Li B (2015) Ant colony optimization applied to web service compositions in cloud computing. Comput Electr Eng 41:18–27
Xia Y, Chen P, Bao L, Wang M, Yang J (2011) A QoS-aware web service selection algorithm based on clustering. In: Web services (ICWS), 2011 IEEE International Conference. IEEE, pp 428–435
Newcomer E, Lomow G (2005) Understanding SOA with web services. Addison-Wesley, Boston, Mass
Ghazanfari M, Alizadeh S, Teymurpour B (2013) Data mining and knowledge discovery. Science and Technology Publication Co, 1st edn. ISBN: 9789644541780
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB, vol 1215, pp 487–499
Han Jiawei, Kamber Micheline, Pei Jian (2011) Data mining: concepts and techniques. Elsevier, Amsterdam
Bianco P, Lewis GA, Merson P (2008) Service Level agreements in service-oriented architecture environments. Technical note CMU/SEI-2008-TN-021. http://www.sei.cmu.edu
van Steen M, Tanenbaum AS (2007) Distributed systems: principles and paradigms
Nai-zhong WU (2013) Dynamic composition of web service based on cloud computing. Int J Hybrid Inf Technol 6(6):389–398
Mitchell Melanie (1998) An introduction to genetic algorithms. MIT Press, Cambridge
Wright AH (1991) Genetic algorithms for real parameter optimization. Found Genet Algorithms 1:205–218
Lim SP, Haron H (2013) Performance comparison of genetic algorithm, differential evolution and particle swarm optimization towards benchmark functions. In: Open Systems (ICOS), 2013 IEEE Conference. IEEE, pp 41–46
Hassan R, Cohanim B, De Weck O, Venter G (2005) A comparison of particle swarm optimization and the genetic algorithm. In: Proceedings of the 1st AIAA Multidisciplinary Design Optimization Specialist Conference, pp 18–21
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Karimi, M.B., Isazadeh, A. & Rahmani, A.M. QoS-aware service composition in cloud computing using data mining techniques and genetic algorithm. J Supercomput 73, 1387–1415 (2017). https://doi.org/10.1007/s11227-016-1814-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-016-1814-8