Abstract
Workflow scheduling is one of the most-focused research problems in the field of cloud computing. This is a well known NP-complete problem and therefore finding an optimal solution in respect of various parameters such as makespan, resource utilization, energy, QoS or their combination is computationally very expensive. Nevertheless, load balancing among the virtual machines (VMs) is one of the most important aspects while scheduling tasks of the workflow. In this paper, we propose a hybrid meta-heuristic approach for workflow scheduling for IaaS cloud which is shown to be load balanced. The proposed algorithm is based on hybridization of genetic algorithm (GA) and particle swarm optimization (PSO). The algorithm takes advantages of both the algorithms by avoiding slower convergence rate of GA and local optimum problem in PSO. The objective of the proposed algorithm is to map the tasks of the workflow to the VMs, such that the overall workflow execution time (makespan) is minimized and the assigned load on each VM is also balanced. With the rigorous experiments on scientific workflows, we show that the proposed approach performs better than PSO, GA and MPQGA (multiple priority queues genetic algorithm) based workflow scheduling algorithms. We also validate the better performance through a statistical test, i.e., paired t test with 95% confidence interval.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmad, S.G., Liew, C.S., Munir, E.U., Ang, T.F., Khan, S.U.: A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems. J. Parallel Distrib. Comput. 87, 80–90 (2016)
Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25(6), 599–616 (2009)
Alkhanak, E.N., Lee, S.P., Rezaei, R., Parizi, R.M.: Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: a review, classifications, and open issues. J. Syst. Softw. 113, 1–26 (2016)
Man, K.-F., Tang, K.-S., Kwong, S.: Genetic algorithms: concepts and applications in engineering design. IEEE Trans. Ind. Electron. 43(5), 519–534 (1996)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: 1995 Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43. IEEE (1995)
Keshanchi, B., Souri, A., Navimipour, N.J.: An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J. Syst. Softw. 124, 1–21 (2017)
Awad, A.I., El-Hefnawy, N.A., Abdel\(\_\)kader, H.M.: Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Comput. Sci. 65, 920–929 (2015)
Li, R., Huang, W., Yuan, Q.: Grid task scheduling using mutation particle swarm algorithm. In: IEEE Conference Anthology, pp. 1–3. IEEE (2013)
Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Futur. Gener. Comput. Syst. 29(3), 682–692 (2013)
Zhu, Z., Zhang, G., Li, M., Liu, X.: Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans. Parallel Distrib. Syst. 27(5), 1344–1357 (2016)
Cho, K.-M., Tsai, P.-W., Tsai, C.-W., Yang, C.-S.: A hybrid meta-heuristic algorithm for vm scheduling with load balancing in cloud computing. Neural Comput. Appl. 26(6), 1297–1309 (2015)
Tao, F., Feng, Y., Zhang, L., Liao, T.W.: CLPS-GA: a case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Appl. Soft Comput. 19, 264–279 (2014)
Zhong, H., Tao, K., Zhang, X.: An approach to optimized resource scheduling algorithm for open-source cloud systems. In: 2010 Fifth Annual ChinaGrid Conference (ChinaGrid), pp. 124–129. IEEE (2010)
Delavar, A.G., Aryan, Y.: HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems. Clust. Comput. 17(1), 129–137 (2014)
Choudhary, A., Gupta, I., Singh, V., Jana, P.K.: A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing. Futur. Gener. Comput. Syst. 83, 14–26 (2018)
He, X.S., Sun, X.H., Von Laszewski, G.: QoS guided min-min heuristic for grid task scheduling. J. Comput. Sci. Technol. 18(4), 442–451 (2003)
Mao, Y., Chen, X., Li, X.: Max–min task scheduling algorithm for load balance in cloud computing. In: Patnaik, S., Li, X. (eds.) CSAIT 2013. AISC, vol. 255, pp. 457–465. Springer, New Delhi (2014). https://doi.org/10.1007/978-81-322-1759-6_53
Zhan, Z.-H., Zhang, G.-Y., Gong, Y.-J., Zhang, J., et al.: Load balance aware genetic algorithm for task scheduling in cloud computing. In: Dick, G., et al. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 644–655. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13563-2_54
Omara, F.A., Arafa, M.M.: Genetic algorithms for task scheduling problem. J. Parallel Distrib. Comput. 70(1), 13–22 (2010)
Wang, X., Yeo, C.S., Buyya, R., Su, J.: Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm. Futur. Gener. Comput. Syst. 27(8), 1124–1134 (2011)
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)
Kumar, M.S., Gupta, I., Panda, S.K., Jana, P.K.: Granularity-based workflow scheduling algorithm for cloud computing. J. Supercomput. 73(12), 5440–5464 (2017)
Xu, Y., Li, K., Hu, J., Li, K.: A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf. Sci. 270, 255–287 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gupta, I., Gupta, S., Choudhary, A., Jana, P.K. (2019). A Hybrid Meta-heuristic Approach for Load Balanced Workflow Scheduling in IaaS Cloud. In: Fahrnberger, G., Gopinathan, S., Parida, L. (eds) Distributed Computing and Internet Technology. ICDCIT 2019. Lecture Notes in Computer Science(), vol 11319. Springer, Cham. https://doi.org/10.1007/978-3-030-05366-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-05366-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05365-9
Online ISBN: 978-3-030-05366-6
eBook Packages: Computer ScienceComputer Science (R0)