A Systematic Review of the Combinatorial Methods of Service Composition in the Cloud Computing ‎Environment

Document Type : Research Paper

Author

Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

Abstract

In the latest decade, the concept of service delivery and software-as-a-service have evolved into two important evolution paradigms which have affected the information systems area. A paradigm shift in software development has been triggered by this change, in which software is developed through the use of ready-made services in the cloud. Since service composition in the cloud environment must be done on-the-fly, realizing this requires a trade-off between the optimality of the composite service and the time it takes. As QoS-aware service composition has several potential solutions, some of which are usually considered optimal, which should be considered an NP-hard problem. A growing number of services leads to a larger problem search space, which is why in recent years many researchers have looked into methods that use meta-heuristic algorithms to solve the problem of service composition in a cloud environment. Thus, it is crucial that researchers have access to up-to-date and specialized review articles. Based on a systematic review of the research literature, the paper aims to extract important questions that are relevant to meta-heuristic QoS-aware service composition methods. Then, after classifying the studies and studying the proposed methods, goals, and priorities of researchers in articles, useful results and statistics for future research in this field are presented.

Keywords

References

 [1] S. P. Roger, R. M. Bruce, Software engineering: a practitioner’s approach, McGraw-Hill Education, (2015).
[2] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, I. Brandic, Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Generation computer systems 25 (2009) 599-616.
[3] R. Buyya, J. Broberg, A. M. Goscinski, Cloud computing: Principles and paradigms, John Wiley & Sons, (2010).
[4] A. Jula, E. Sundararajan, Z. Othman, Cloud computing service composition: A systematic literature review, Expert systems with applications 41 (2014) 3809-3824.
[5] M. B. Karimi, A. Isazadeh, A. M. Rahmani, QoS-aware service composition in cloud computing using data mining techniques and genetic algorithm, The Journal of Supercomputing 73 (2017) 1387-1415.
[6] F. Liu, NIST cloud computing reference architecture, NIST special publication 500 (2011) 1-28.
[7] S. K. Garg, S. Versteeg, R. Buyya, A framework for ranking of cloud computing ser vices, Future Generation Computer Systems 29 (2013) 1012-1023.
[8] K. P. Joshi, Y. Yesha, T. Finin, Automating cloud services life cycle through semantic technologies, IEEE Transactions on Services Computing 7 (2012) 109-122.
[9] S. A. Baset, Cloud SLAs: present and future, ACM SIGOPS Operating Systems Review 46 (2012) 57-66.
[10] M. Teixeira, R. Ribeiro, C. Oliveira, R. Massa, A quality-driven approach for resources planning in service-oriented architectures, Expert Systems with Applications 42 (2015) 5366-5379.
[11] A. S. da Silva, H. Ma, M. Zhang, Genetic programming for QoS-aware web service composition and selection, Soft Computing 20 (2016) 3851-3867.
[12] F. Tao, D. Zhao, Y. Hu, Z. Zhou, Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system, IEEE Transactions on industrial informatics 4 (2008) 315-327.
[13] M. Alrifai, T. Risse, W. Nejdl, A hybrid approach for efficient Web service composition with end-to-end QoS constraints, ACM Transactions on the Web (TWEB) 6 (2012) 1-31.
[14] V. Hayyolalam, A. A. P. Kazem, A systematic literature review on QoS-aware service composition and selection in cloud environment, Journal of Network and Computer Applications 110 (2018) 52-74.
[15] Z. Ye, X. Zhou, A. Bouguettaya, Genetic algorithm based QoS-aware service compositions in cloud computing, International Conference on Database Systems for Advanced Applications 13 (2011) 321-334.
[16] F. Moscato, N. Mazzocca, V. Vittorini, G. D. Lorenzo, P. Mosca, M. Magaldi, Workflow pattern analysis in web services orchestration: The BPEL4WS example, International Conference on High Performance Computing and Communications 15 (2005) 395-400.
[17] J. Huang, Q. Duan, S. Guo, Y. Yan, S. Yu, Converged network-cloud service composition with end-to-end performance guarantee, IEEE Transactions on Cloud Computing 6 (2015) 545-557.
[18] 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 326 (2016) 315-333.
[19] J. Qi, B. Xu, Y. Xue, K. Wang, Y. Sun, Knowledge based differential evolution for cloud computing service composition, Journal of Ambient Intelligence and Humanized Computing 9 (2018) 565-574.
[20] C. Jatoth, G. Gangadharan, U. Fiore, Optimal fitness aware cloud service composition using modified invasive weed optimization, Swarm and evolutionary computation 44 (2019) 1073-1091.
[21] C. Jatoth, G. Gangadharan, R. Buyya, Optimal fitness aware cloud service composition using an adaptive genotypes evolution based genetic algorithm, Future Generation Computer Systems 94 (2019) 185-198.
[22] S. K. Gavvala, C. Jatoth, G. Gangadharan, R. Buyya, QoS-aware cloud service composition using eagle strategy, Future Generation Computer Systems 90 (2019) 273-290.
[23] F. Dahan, An effective multi-agent ant colony optimization algorithm for QoSaware cloud service composition, IEEE Access 9 (2021) 17196-17207.
[24] H. Tarawneh, I. Alhadid, S. Khwaldeh, S. Afaneh, An Intelligent Cloud Service Composition Optimization Using Spider Monkey and Multistage Forward Search Algorithms, Symmetry 14 (2022) 82-98.
[25] F. Tao, Y. LaiLi, L. Xu, L. Zhang, FCPACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system, IEEE Transactions on Industrial Informatics 9 (2012) 2023-2033.
[26] D. Wang, Y. Yang, Z. Mi, A genetic-based approach to web service composition in geodistributed cloud environment, Computers & Electrical Engineering 43 (2015) 129-141.
[27] H. Kurdi, A. Al-Anazi, C. Campbell, A. Al Faries, A combinatorial optimization algorithm for multiple cloud service composition, Computers & Electrical Engineering 42 (2015) 107-113.
[28] 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 42 (2015) 135-145.
[29] Q. Yu, L. Chen, B. Li, Ant colony optimization applied to web service compositions in cloud computing, Computers & Electrical Engineering 41 (2015) 18-27.
[30] F. Chen, R. Dou, M. Li, H. Wu, A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing, Computers & Industrial Engineering 99 (2016) 423-431.
[31] F. Seghir, A. Khababa, A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition, Journal of Intelligent Manufacturing 29 (2018) 1773-1792.
[32] M. Ghobaei-Arani, A. A. Rahmanian, M. S. Aslanpour, S. E. Dashti, CSA-WSC: cuckoo search algorithm for web service composition in cloud environments, Soft Computing 22 (2018) 8353-8378.
[33] L. Liu, S. Gu, D. Fu, M. Zhang, R. Buyya, A new multi-objective evolutionary algorithm for inter-cloud service composition, KSII Transactions on Internet and Information Systems (TIIS) 12 (2018) 1-20.
[34] J. Zhou, X. Yao, Y. Lin, F. T. Chan, Y. Li, An adaptive multi-population differential artificial bee colony algorithm for manyobjective service composition in cloud manufacturing, Information Sciences 456 (2018) 50-82.
[35] G. J. Ibrahim, T. A. Rashid, M. O. Akinsolu, An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment, Journal of parallel and distributed computing 143 (2020) 77-87.
[36] E. Al-Masri, Q. H. Mahmoud, Investigating web services on the world wide web, Proceedings of the 17th international conference on World Wide Web 20 (2008) 795-804.
[37] OWLS-Xplan Service Composition Planner.
[38] A. G. M Klusch, OWLS-Xplan Service Composition Planner, (2006).
[39] Z. Zheng, Y. Zhang, M. R. Lyu, Distributed qos evaluation for real-world web services, 2010 IEEE International Conference on Web Services 21 (2010) 83-90.
International Journal of Industrial Mathematics
Volume 14, Issue 4
September 2022
Pages 489-502

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