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Wireless Communications and Mobile Computing
Volume 2018, Article ID 1934784, 16 pages
https://doi.org/10.1155/2018/1934784
Research Article

Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing

1Network and Information Security Department, Yarmouk University, Irbid 21163, Jordan
2Computer Sciences Department, Yarmouk University, Irbid 21163, Jordan

Correspondence should be addressed to Ahmad M. Manasrah; oj.ude.uy@a.damha

Received 27 September 2017; Accepted 11 December 2017; Published 8 January 2018

Academic Editor: B. B. Gupta

Copyright © 2018 Ahmad M. Manasrah and Hanan Ba Ali. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. H. Aljammal, A. M. Manasrah, A. E. Abdallah, and N. M. Tahat, “A new architecture of cloud computing to enhance the load balancingg,” International Journal of Business Information Systems, vol. 25, no. 3, pp. 393–405, 2007. View at Google Scholar
  2. J. Li, Z. Liu, X. Chen, F. Xhafa, X. Tan, and D. S. Wong, “L-EncDB: A lightweight framework for privacy-preserving data queries in cloud computing,” Knowledge-Based Systems, vol. 79, pp. 18–26, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. A. M. Manasrah, T. Smadi, and A. ALmomani, “A Variable Service Broker Routing Policy for data center selection in cloud analyst,” Journal of King Saud University - Computer and Information Sciences, vol. 29, no. 3, pp. 365–377, 2017. View at Publisher · View at Google Scholar · View at Scopus
  4. B. B. Gupta and T. Akhtar, “A survey on smart power grid: frameworks, tools, security issues, and solutions,” Annales des Télécommunications, vol. 72, no. 9-10, pp. 517–549, 2017. View at Publisher · View at Google Scholar
  5. J. Yu, R. Buyya, and K. Ramamohanarao, “Workflow scheduling algorithms for grid computing,” in Metaheuristics for scheduling in distributed computing environments, pp. 173–214, Springer, 2008. View at Google Scholar
  6. A. Verma and S. Kaushal, “Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud,” Journal of Grid Computing, vol. 13, no. 4, pp. 495–506, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Ji, W. Bao, and X. Zhu, “Adaptive workflow scheduling for diverse objectives in cloud environments,” Transactions on Emerging Telecommunications Technologies, vol. 28, no. 2, Article ID e2941, 2017. View at Publisher · View at Google Scholar · View at Scopus
  8. A. M. Manasrah, “Dynamic weighted VM load balancing for cloud-analyst,” International Journal of Information and Computer Security, vol. 9, no. 1-2, pp. 5–19, 2017. View at Publisher · View at Google Scholar · View at Scopus
  9. W.-N. Chen and J. Zhang, “An ant colony optimization approach to a grid workflow scheduling problem with various QoS requirements,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 39, no. 1, pp. 29–43, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. A. K. M. K. A. Talukder, M. Kirley, and R. Buyya, “Multiobjective differential evolution for scheduling workflow applications on global Grids,” Concurrency and Computation: Practice and Experience, vol. 21, no. 13, pp. 1742–1756, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Wieczorek, A. Hoheisel, and R. Prodan, “Towards a general model of the multi-criteria workflow scheduling on the grid,” Future Generation Computer Systems, vol. 25, no. 3, pp. 237–256, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Li, J. Li, Z. Huang, C.-Z. Gao, W.-B. Chen, and K. Chen, “Privacy-preserving outsourced classification in cloud computing,” Cluster Computing, pp. 1–10, 2017. View at Publisher · View at Google Scholar · View at Scopus
  13. C. Stergiou, K. E. Psannis, B.-G. Kim, and B. Gupta, “Secure integration of IoT and Cloud Computing,” Future Generation Computer Systems, vol. 78, pp. 964–975, 2018. View at Publisher · View at Google Scholar · View at Scopus
  14. K. Dasgupta, B. Mandal, P. Dutta, J. K. Mandal, and S. Dam, “A genetic algorithm (GA) based load balancing strategy for cloud computing,” Procedia Technology, vol. 10, pp. 340–347, 2013. View at Google Scholar
  15. Z. Zhang and X. Zhang, “A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation,” in Proceedings of the 2nd International Conference on Industrial Mechatronics and Automation (ICIMA '10), vol. 2, pp. 240–243, May 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. T. D. Braun, H. J. Siegel, N. Beck et al., “A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems,” Journal of Parallel and Distributed Computing, vol. 61, no. 6, pp. 810–837, 2001. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Rana, S. Bilgaiyan, and U. Kar, “A study on load balancing in cloud computing environment using evolutionary and swarm based algorithms,” in Proceedings of the 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies, ICCICCT 2014, pp. 245–250, India, July 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Zhu, G. Zhang, M. Li, and X. Liu, “Evolutionary multi-objective workflow scheduling in cloud,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 5, pp. 1344–1357, 2016. View at Publisher · View at Google Scholar
  19. Y. Mao, X. Chen, and X. Li, “Max–Min task scheduling algorithm for load balance in cloud computing,” in Proceedings of International Conference on Computer Science and Information Technology, S. Patnaik and X. Li, Eds., vol. 225, pp. 457–465, Springer, New Delhi, India, 2014. View at Publisher · View at Google Scholar
  20. P. Kumar and A. Verma, “Scheduling using improved genetic algorithm in cloud computing for independent tasks,” in Proceedings of the 2012 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2012, pp. 137–142, India, August 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Guo, S. Zhao, S. Shen, and C. Jiang, “Task scheduling optimization in cloud computing based on heuristic algorithm,” Journal of Networks, vol. 7, no. 3, pp. 547–553, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. L. Zhang, Y. Chen, R. Sun, S. Jing, and B. Yang, “A task scheduling algorithm based on PSO for grid computing,” International Journal of Computational Intelligence Research, vol. 4, no. 1, pp. 37–43, 2008. View at Google Scholar
  23. S. Pandey, L. Wu, S. M. Guru, and R. Buyya, “A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments,” in Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, AINA2010, pp. 400–407, Australia, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Arabnejad and J. G. Barbosa, “A Budget Constrained Scheduling Algorithm for Workflow Applications,” Journal of Grid Computing, vol. 12, no. 4, pp. 665–679, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. H. Xu, B. Yang, W. Qi, and E. Ahene, “A multi-objective optimization approach to workflow scheduling in clouds considering fault recovery,” KSII Transactions on Internet & Information Systems, vol. 10, no. 3, 2016. View at Google Scholar
  26. S. Chitra, B. Madhusudhanan, G. R. Sakthidharan, and P. Saravanan, “Local minima jump PSO for workflow scheduling in cloud computing environments,” Lecture Notes in Electrical Engineering, vol. 279, pp. 1225–1234, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. Y. Ge and G. Wei, “GA-based task scheduler for the cloud computing systems,” in Proceedings of the International Conference on Web Information Systems and Mining (WISM '10), vol. 2, pp. 181–186, IEEE, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. H. M. Fard, R. Prodan, J. J. D. Barrionuevo, and T. Fahringer, “A multi-objective approach for workflow scheduling in heterogeneous environments,” in Proceedings of the 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2012, pp. 300–309, Canada, May 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. Z. Wu, X. Liu, Z. Ni, D. Yuan, and Y. Yang, “A market-oriented hierarchical scheduling strategy in cloud workflow systems,” The Journal of Supercomputing, vol. 63, no. 1, pp. 256–293, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. H. Ba_ali, Workflow Load Balancing and Scheduling using Genetic Algorith (GA) and Particle Swarm Optimization (PSO) in Cloud Computing, Yarmouk University, Irbid, Jordan, 2017.
  31. W. Zheng and R. Sakellariou, “Budget-Deadline Constrained Workflow Planning for Admission Control,” Journal of Grid Computing, vol. 11, no. 4, pp. 633–651, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. J. C. Jacob, D. S. Katz, T. Prince et al., The Montage Architecture for Grid-Enabled Science Processing of Large, Distributed Datasets, Jet Propulsion Laboratory, National Aeronautics and Space Administration, Pasadena, Clif, USA, 2004.
  33. H. Magistrale, S. Day, R. W. Clayton, and R. Graves, “The SCEC southern California reference three-dimensional seismic velocity model version 2,” Bulletin of the Seismological Society of America, vol. 90, no. 6, pp. S65–S76, 2000. View at Publisher · View at Google Scholar · View at Scopus
  34. E. Deelman, K. Vahi, G. Juve et al., “Pegasus, a workflow management system for science automation,” Future Generation Computer Systems, vol. 46, pp. 17–35, 2015. View at Publisher · View at Google Scholar · View at Scopus
  35. D. A. Brown, P. R. Brady, A. Dietz, J. Cao, B. Johnson, and J. McNabb, “A case study on the use of workflow technologies for scientific analysis: Gravitational wave data analysis,” in Workflows for e-Science, pp. 39–59, Springer, 2007. View at Google Scholar
  36. J. Livny, H. Teonadi, M. Livny, and M. K. Waldor, “High-throughput, kingdom-wide prediction and annotation of bacterial non-coding RNAs,” PLoS ONE, vol. 3, no. 9, Article ID e3197, 2008. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Alajmi and J. Wright, “Selecting the most efficient genetic algorithm sets in solving unconstrained building optimization problem,” International Journal of Sustainable Built Environment, vol. 3, no. 1, pp. 18–26, 2014. View at Publisher · View at Google Scholar · View at Scopus
  38. W. Chen and E. Deelman, “WorkflowSim: A toolkit for simulating scientific workflows in distributed environments,” in Proceedings of the 2012 IEEE 8th International Conference on E-Science, e-Science 2012, USA, October 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. de Rose, and R. Buyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software: Practice and Experience, vol. 41, no. 1, pp. 23–50, 2011. View at Publisher · View at Google Scholar · View at Scopus
  40. A. Ghorbannia Delavar and Y. Aryan, “HSGA: A hybrid heuristic algorithm for workflow scheduling in cloud systems,” Cluster Computing, vol. 17, no. 1, pp. 129–137, 2014. View at Publisher · View at Google Scholar · View at Scopus
  41. D. G. Amalarethinam and T. L. A. Beena, “Workflow Scheduling for Public Cloud Using Genetic Algorithm (WSGA),” IOSR Journals (IOSR Journal of Computer Engineering), vol. 1, no. 18, pp. 23–27, 2016. View at Google Scholar