Abstract
The Cloud workflow scheduling is to find proper Cloud resources for the execution of workflow tasks to efficiently utilize resources and meet different user’s quality of service requirements. Cloud workflow scheduling is a constrained and NP-complete problem and multi-objective evolutionary algorithms have shown their excellent ability to solve such problem. But most existing works simply use static penalty function to handle constraints which usually result in premature when the constraints become strict. On the other hand, with the search space being more tremendous and chaotic, how to balance the ability of exploring the entire search space and exploiting the important regions during the evolutionary process is increasingly important. In this paper, an adaptive individual-assessment scheme based on evolutionary states is proposed to handle the constraints in multi-objective optimization problems. In addition, the evolutionary parameters are also adjusted accordingly to balance the exploration and exploitation ability. These are distinguishable from most previous studies that directly incorporate multi-objective evolutionary algorithm to search excellent solutions for Cloud workflow scheduling. Experimental results demonstrate the proposed algorithm outperforms other state-of-the-art methods in convergence and diversity, and it also achieves better optimization ability when it is applied to solve Cloud workflow scheduling problem.
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Vöckler, J.S., Juve, G., Deelman, E., et al.: Experiences using cloud computing for a scientific workflow application, pp. 15–24. International Workshop on Scientific Cloud, Computing (2011)
Zhu, Z., Zhang, G., Li, M., et al.: Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans. Parallel Distrib. Syst. 27(5), 1344–1357 (2016)
Juve, G., Chervenak, A., Deelman, E., et al.: Characterizing and profiling scientific workflows. Future Gener. Comput. Syst. 29(3), 682–692 (2013)
Buyya, R., Yeo, C.S., Venugopal, S., et al.: 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 (2009)
Foster, I., Zhao, Y., Raicu, I., et al.: Cloud computing and grid computing 360-degree compared. Grid Computing Environments Workshop, 2008. GCE ’08, pp. 1–10. IEEE, New York (2009)
Wang, X., Yeo, C.S., Buyya, R., et al.: Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm. Future Gener. Comput. Syst. 27(8), 1124–1134 (2011)
Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Liu, L., Zhang, M.: Multi-objective optimization model with AHP decision-making for Cloud service composition. Ksii Trans. Internet Inform. Syst. 9(9), 3293–3311 (2015)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2008)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Springer, Berlin (2001)
Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)
Rodriguez, M.A., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on Clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)
Malawski, M., Juve, G., Deelman, E., et al.: Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds. In: SC ’12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, pp. 1–18 (2012)
Liu, L., Zhang, M., Lin, Y., et al.: A survey on workflow management and scheduling in cloud computing. IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 837–846. ACM, New York (2014)
Deng, K., Ren, K., Zhu, M., et al.: A data and task co-scheduling algorithm for scientific Cloud workflows. IEEE Trans. Cloud Comput. 7161, 1 (2015)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co. Inc., Boston (1989)
Zhang, J., Chung, S.H., Lo, W.L.: Clustering-based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Trans. Evol. Comput. 11(3), 326–335 (2007)
Hu, W., Yen, G.G.: Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system. IEEE Trans. Evol. Comput. 19(1), 1–18 (2015)
Inselberg, A.: The plane with parallel coordinates. Visual Comput. 1(2), 69–91 (1985)
Hu, W., Yen, G.G., Zhang, X.: Multiobjective particle swarm optimization based on Pareto entropy. J. Softw. 25(5), 1025–1050 (2014). (in Chinese)
Toombs, R., Reed, J., Barricelli, N.A.: Simulation of biological evolution and machine learning. J. Theor. Biol. 17(3), 319 (1967)
Zhi-Hui, Z., Jun, Z., Yun, L., et al.: Adaptive particle swarm optimization. IEEE Trans. Syst. Man Cybern. Part B 39(6), 1362–81 (2009)
Calheiros, R.N., Ranjan, R., Beloglazov, A., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)
Chen, W., Deelman, E.: WorkflowSim: a toolkit for simulating scientific workflows in distributed environments. In: IEEE International Conference on E-Science. IEEE, pp. 1–8 (2012)
Nanakorn, P., Meesomklin, K.: An adaptive penalty function in genetic algorithms for structural design optimization. Comput. Struct. 79(29–30), 2527–2539 (2001)
Tessema, B., Yen, G.G.: An adaptive penalty formulation for constrained evolutionary optimization. IEEE Trans. Syst. Man Cybern. Part A 39(3), 565–578 (2009)
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)
Zhang, Q., Zhou, A., Zhao, S., et al.: Multiobjective optimization test instances for the CEC 2009 special session and competition. Technical Report, University of Essex (2008)
Deb, K., Sinha, A., Kukkonen, S.: Multi-objective test problems, linkages, and evolutionary methodologies. In: Genetic and Evolutionary Computation Conference, GECCO 2006, Proceedings, Seattle, Washington, USA, pp. 1141–1148 (2006)
Deb, K., Kalyanmoy, D.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)
He, Q., Wang, L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng. Appl. Artif. Intell. 20(1), 89–99 (2007)
Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)
Zheng, W., Sakellariou, R.: Budget-deadline constrained workflow planning for admission control. J. Grid Comput. 11(4), 105–119 (2013)
Arabnejad, H., Barbosa, J.G.: A budget constrained scheduling algorithm for workflow applications. J. Grid Comput. 12(4), 15 (2014)
Durillo, J.J., Prodan, R.: Multi-objective workflow scheduling in amazon ec2. Cluster Comput. 17(2), 169–189 (2014)
Sawant, S.: A genetic algorithm scheduling approach for virtual machine resources in a Cloud computing environment. Master thesis, San Jose State University (2011)
Garg, R., Singh, A.K.: multi-objective workflow grid scheduling based on discrete particle swarm optimization. In: International Conference, Semcco, Visakhapatnam, Andhra Pradesh, India, December, pp. 183–190 (2011)
Garg, R., Singh, A.K.: Multi-objective workflow grid scheduling using \(\epsilon \)-fuzzy dominance sort based discrete particle swarm optimization. J. Supercomput. 68(2), 709–732 (2014)
Yu, J., Kirley, M., Buyya, R.: Multi-objective planning for workflow execution on Grids. In: IEEE/ACM International Conference on Grid Computing. IEEE Computer Society, pp. 10–17 (2007)
Wang, X., Xia, JW., Li, JZ., et al.: Grid workflow scheduling with various QoS constraints using SPEA2+. In: Fourth International Conference on Genetic and Evolutionary Computing. IEEE Xplore, pp. 829–832 (2011)
Bader, J., Zitzler, E.: HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1), 45–76 (2011)
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Zhang, M., Li, H., Liu, L. et al. An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in Clouds. Distrib Parallel Databases 36, 339–368 (2018). https://doi.org/10.1007/s10619-017-7215-z
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DOI: https://doi.org/10.1007/s10619-017-7215-z