Abstract
Cloud computing is a popular model that allows users to store, access, process, and retrieve data remotely. It provides a high-performance computing with large scale of resources. However, this model requires an efficient scheduling strategy for resources management. Recently, several algorithms are presented to solve the resource scheduling problem. Nevertheless, still the problem exists with complex applications such as workflows, which need an efficient algorithm to be scheduled on the available resources. This paper presents a novel hybrid algorithm, called CR-AC, combining both the chemical reaction optimization (CRO) and ant colony optimization (ACO) algorithms to solve the workflow-scheduling problem. The proposed CR-AC algorithm is implemented in the CloudSim toolkit and evaluated by using real applications and Amazon EC2 pricing model. Moreover, the results are compared with the most recent algorithms: modified particle swarm optimization (PSO) and cost-effective genetic algorithm (CEGA). The experimental results indicate that the CR-AC algorithm achieves better results than the traditional CRO, the ACO, the modified PSO and CEGA algorithms, in terms of total cost, time complexity, and schedule length.
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References
Mei, L.; Chan, W.K.; Tse, T.H.: A tale of clouds: paradigm comparisons and some thoughts on research issues. Proc. APSCC 2008, 464–469 (2008)
Haijun, Z.; Cao, X.; Ho, J.K.L.; Chow, T.W.S.: Object-level video advertising: an optimization framework. IEEE Trans. Ind. Inform. 13(2), 520–531 (2017)
Haijun, Z.; Llorca, J.; Davis, C.C.; Milner, S.D.: Nature-inspired self-organization, control, and optimization in heterogeneous wireless networks. IEEE Trans. Mob. Comput. 11(7), 1207–1222 (2012)
Nasr, A.A.; El-Bahnasawy, N.A.; El-Sayed, A.: Task scheduling optimization in heterogeneous distributed systems. Int. J. Comput. Appl. 107(4), 5–12 (2014)
Deelman, E.; Vahi, K.; Juve, G.; Rynge, M.; Callaghan, S.; Maechling, P.J.; Mayani, R.; Chen, W.; Ferreira da Silva, R.; Livny, M.; Wenger, K.: Pegasus: a workflow management system for science automation. Future Gener. Comput. Syst. 46, 17–35 (2015)
Xu, Y.; Li, K.; He, L.; Truong, T.K.: A DAG scheduling scheme on heterogeneous computing systems using double molecular structure-based chemical reaction optimization. J. Parallel Distrib. Comput. 73, 1306–1322 (2013)
Amalarethinam, D.I.G.; Lucia Agnes Beena, T.: Customer facilitated cost-based scheduling (CFCSC) in cloud. Proc. Comput. Sci. 46, 660–667 (2015)
Elsherbiny, S.; Eldaydamony, E.; Alrahmawy, M.; Reyad, A.E.: An extended intelligent water drops algorithm for workflow scheduling in cloud computing environment. Egypt. Inform. J. 19, 1–23 (2017)
Visheratin, A.A.; Melnik, M.; Nasonov, D.: Workflow scheduling algorithms for hard-deadline constrained cloud environments. Proc. Comput. Sci. 80, 2098–2106 (2016)
Arabnejad, H.; Barbosa, J.G.: A budget constrained scheduling algorithm for workflow applications. J. Grid Comput. 12, 665–679 (2014)
Zhu, Z.; Zhang, G.; Li, M.; Liu, X.: Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans. Parallel Distrib. Syst. 27, 1344–1357 (2016)
Xiang, B.; Zhang, B.; Zhang, L.: Greedy-ant: ant colony system-inspired workflow scheduling for heterogeneous computing. IEEE Access 5, 11404–11412 (2017)
Khalili, A.; Babamir, S.M.: Optimal scheduling workflows in cloud computing environment using Pareto based Grey Wolf Optimizer. Concurr. Comput. Pract. Exp. 29, 1–11 (2017)
Verma, A.; Kaushal, S.: Cost minimized PSO based workflow scheduling plan for cloud computing. Int. J. Inf. Technol. Comput. Sci. 8, 37–43 (2015)
Meena, J.; Kumar, M.; Vardhan, M.: Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint. IEEE Access 4, 5065–5082 (2016)
Nasr, A.A.; El-Bahnasawy, N.A.; Attiya, G.; El-Sayed, A.: Using the TSP solution strategy for cloudlet scheduling in cloud computing. J. Netw. Syst. Manag. 1–22, 2018 (2018)
Bidaki, M.; Tabbakh, S.R.K.; Yaghoobi, M.; Shakeri, H.: Secure and efficient SOS-based workflow scheduling in cloud computing. Int. J. Secur. Its Appl. 11(3), 41–58 (2017)
Nasr, A.A.; EL-Bahnasawy, N.A.; EL-Sayed, A.: A new duplication task scheduling algorithm in heterogeneous distributed computing systems. Bull. Electr. Eng. Inform. 5(3), 373–382 (2016)
Xu, J.; Lam, A.Y.S.; Li, V.O.K.: Chemical reaction optimization for task scheduling in grid computing. IEEE Trans. Parallel Distrib. Syst. 22(10), 1624–1631 (2011)
Liu, C.; Zou, C.; Wu, P.: A task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing. In: Proceedings of the 13th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES) (2014)
https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator
Juve, G.; Chervenak, A.; Deelman, E.; Bharathi, S.; Mehta, G.; Vahi, K.: Characterizing and profiling scientific workflows. Future Gener. Comput. Syst. 29(3), 682–692 (2013)
Calheiros, R.N.; Ranjan, R.; Beloglazov, A.; De Rose, C.A.; Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41, 23–50 (2010)
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Nasr, A.A., El-Bahnasawy, N.A., Attiya, G. et al. Cost-Effective Algorithm for Workflow Scheduling in Cloud Computing Under Deadline Constraint. Arab J Sci Eng 44, 3765–3780 (2019). https://doi.org/10.1007/s13369-018-3664-6
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DOI: https://doi.org/10.1007/s13369-018-3664-6