CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing

by Ibrahim Attiya, Xiaotong Zhang
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 9
Year of Publication: 2017
Authors: Ibrahim Attiya, Xiaotong Zhang
10.5120/ijca2017913744

Ibrahim Attiya, Xiaotong Zhang . A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing. International Journal of Computer Applications. 163, 9 ( Apr 2017), 34-41. DOI=10.5120/ijca2017913744

@article{ 10.5120/ijca2017913744,
author = { Ibrahim Attiya, Xiaotong Zhang },
title = { A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 9 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 34-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume163/number9/27426-2017913744/ },
doi = { 10.5120/ijca2017913744 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:10:00.930732+05:30
%A Ibrahim Attiya
%A Xiaotong Zhang
%T A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 9
%P 34-41
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recent advances in various areas such as networking, information and communication technologies have greatly boosted the potential capabilities of cloud computing and made it become more prevalent in recent years. Cloud computing is a promising computing paradigm that facilitates the delivery of IT infrastructure, platforms, and applications of any kind to consumers as services over the internet. Although cloud computing systems nowadays provide better ways to accomplish the job requests in terms of responsiveness and scalability under various workloads, scheduling of jobs or tasks in cloud environment is still NP-complete and complex in nature due to the dynamicity of resources and on-demand user application requirements. In this paper, a simplified version of particle swarm optimization (PSO) algorithm is proposed to solve the job scheduling problem in cloud computing environment. To evaluate the performance of the proposed approach, this study compares the proposed PSO strategy with genetic algorithm (GA), by having both of them implemented on CloudSim toolkit. The results obtained demonstrate that the presented PSO algorithm can significantly reduce the makespan of job scheduling problem compared with the other metaheuristic algorithm evaluated in this paper.

References
  1. P. M. Mell and T. Grance, “SP 800-145. The NIST Definition of Cloud Computing,” National Institute of Standards & Technology, Gaithersburg, MD, United States, 2011.
  2. I. Attiya and X. Zhang, “Cloud Computing Technology: Promises and Concerns,” Int. J. Comput. Appl., vol. 159, no. 9, pp. 32–37, Feb. 2017.
  3. D.-K. Kang, S.-H. Kim, C.-H. Youn, and M. Chen, “Cost adaptive workflow scheduling in cloud computing,” in Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication - ICUIMC ’14, 2014, pp. 1–8.
  4. Q. Zhang, L. Cheng, and R. Boutaba, “Cloud computing: State-of-the-art and research challenges,” J. Internet Serv. Appl., vol. 1, no. 1, pp. 7–18, 2010.
  5. B. P. Rimal, E. Choi, and I. Lumb, “A Taxonomy and Survey of Cloud Computing Systems,” in 2009 Fifth International Joint Conference on INC, IMS and IDC, 2009, pp. 44–51.
  6. R. Buyya, S. Pandey, and C. Vecchiola, “Cloudbus Toolkit for Market-Oriented Cloud Computing,” in Proceedings of the 1st International Conference on Cloud Computing, M. G. Jaatun, G. Zhao, and C. Rong, Eds. Berlin, Heidelberg: Springer-Verlag, 2009, pp. 24–44.
  7. H.-L. Truong and S. Dustdar, “Cloud computing for small research groups in computational science and engineering: current status and outlook,” Computing, vol. 91, no. 1, pp. 75–91, Jan. 2011.
  8. X. Liu, Z. Zhan, K. Du, and W. Chen, “Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach,” in Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO ’14, 2014, pp. 41–48.
  9. Z. Xiao, W. Song, and Q. Chen, “Dynamic resource allocation using virtual machines for cloud computing environment,” IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 6, pp. 1107–1117, 2013.
  10. A. Ghorbannia Delavar and Y. Aryan, “HSGA: A hybrid heuristic algorithm for workflow scheduling in cloud systems,” Cluster Comput., vol. 17, no. 1, pp. 129–137, 2014.
  11. M. Kalra and S. Singh, “A review of metaheuristic scheduling techniques in cloud computing,” Egypt. Informatics J., vol. 16, no. 3, pp. 275–295, Nov. 2015.
  12. S. R. Shishira, A. Kandasamy, and K. Chandrasekaran, “Survey on meta heuristic optimization techniques in cloud computing,” in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016, pp. 1434–1440.
  13. S. N. Srirama, O. Batrashev, P. Jakovits, and E. Vainikko, “Scalability of parallel scientific applications on the cloud,” Sci. Program., vol. 19, no. 2–3, pp. 91–105, 2011.
  14. M. G. Avram, “Advantages and Challenges of Adopting Cloud Computing from an Enterprise Perspective,” Procedia Technol., vol. 12, pp. 529–534, 2014.
  15. H. Zhang, P. Li, Z. Zhou, and X. Yu, “A PSO-Based Hierarchical Resource Scheduling Strategy on Cloud Computing,” in Trustworthy Computing and Services: International Conference, ISCTCS 2012, Beijing, China, May 28 -- June 2, 2012, Revised Selected Papers, Y. Yuan, X. Wu, and Y. Lu, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 325–332.
  16. W. Li, J. Tordsson, and E. Elmroth, “Modeling for dynamic cloud scheduling via migration of virtual machines,” in Proceedings - 2011 3rd IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2011, 2011, pp. 163–171.
  17. M. Masdari, F. Salehi, M. Jalali, and M. Bidaki, “A Survey of PSO-Based Scheduling Algorithms in Cloud Computing,” J. Netw. Syst. Manag., vol. 25, no. 1, pp. 122–158, Jan. 2017.
  18. Z.-H. Zhan, X.-F. Liu, Y.-J. Gong, J. Zhang, H. S.-H. Chung, and Y. Li, “Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches,” ACM Comput. Surv., vol. 47, no. 4, pp. 1–33, 2015.
  19. R. Nallakumar, N. Sengottaiyan, and S. P. K. S, “A Survey on Scheduling and the Attributes of Task Scheduling in the Cloud,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 3, no. 10, pp. 8167–8171, 2014.
  20. C. Lin and S. Lu, “Scheduling scientific workflows elastically for cloud computing,” in Proceedings - 2011 IEEE 4th International Conference on Cloud Computing, CLOUD 2011, 2011, pp. 746–747.
  21. M. L. Pinedo, Scheduling: Theory, algorithms, and systems: Fourth edition. Boston, MA: Springer US, 2012.
  22. D. M. Lei, “Minimizing makespan for scheduling stochastic job shop with random breakdown,” Appl. Math. Comput., vol. 218, no. 24, pp. 11851–11858, 2012.
  23. S. S. Kim, J. H. Byeon, H. Yu, and H. Liu, “Biogeography-based optimization for optimal job scheduling in cloud computing,” Appl. Math. Comput., vol. 247, pp. 266–280, 2014.
  24. C. W. Tsai and J. J. P. C. Rodrigues, “Metaheuristic scheduling for cloud: A survey,” IEEE Syst. J., vol. 8, no. 1, pp. 279–291, 2014.
  25. J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. Cambridge, MA, USA: MIT Press, 1992.
  26. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing,” Science (80-. )., vol. 220, no. 4598, pp. 671–680, May 1983.
  27. F. Glover, “Future Paths for Integer Programming and Links to Artificial Intelligence,” Comput. Oper. Res., vol. 13, no. 5, pp. 533–549, May 1986.
  28. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, 1995, vol. 4, pp. 1942–1948.
  29. M. Dorigo and L. M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 53–66, Apr. 1997.
  30. A. Walker, J. Hallam, and D. Willshaw, “Bee-havior in a mobile robot: the construction of a self-organized cognitive map and its use in robot navigation within a complex, natural environment,” in IEEE International Conference on Neural Networks, 1993, pp. 1451–1456 vol.3.
  31. F. Pop and M. Potop-Butucaru, “ARMCO: Advanced topics in resource management for ubiquitous cloud computing: An adaptive approach,” Futur. Gener. Comput. Syst., vol. 54, pp. 79–81, Jan. 2016.
  32. S. Banerjee, I. Mukherjee, and P. K. Mahanti, “Cloud Computing Initiative using Modified Ant Colony Framework,” Eng. Technol., vol. 56, no. 8, pp. 221–224, 2009.
  33. L. D. Dhinesh Babu and P. Venkata Krishna, “Honey bee behavior inspired load balancing of tasks in cloud computing environments,” Appl. Soft Comput. J., vol. 13, no. 5, pp. 2292–2303, 2013.
  34. E. Pacini, C. Mateos, C. G. Garino, C. Careglio, and A. Mirasso, “A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds,” J. Intell. Fuzzy Syst., vol. 31, no. 3, pp. 1731–1743, 2016.
  35. X. Wang, C. S. Yeo, R. Buyya, and J. Su, “Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm,” Futur. Gener. Comput. Syst., vol. 27, no. 8, pp. 1124–1134, Oct. 2011.
  36. S. A. A. A. Nada M. Al Sallami Ali Al daoud, “Load Balancing with Neural Network,” Int. J. Adv. Comput. Sci. Appl., vol. 4, no. 10, pp. 138–145, 2013.
  37. C. Zhao, S. Zhang, Q. Liu, J. Xie, and J. Hu, “Independent Tasks Scheduling Based on Genetic Algorithm in Cloud Computing,” in 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, 2009, pp. 1–4.
  38. S. Selvarani and G. S. Sadhasivam, “Improved cost-based algorithm for task scheduling in cloud computing,” in 2010 IEEE International Conference on Computational Intelligence and Computing Research, 2010, pp. 1–5.
  39. C.-W. Tsai, W.-C. Huang, M.-H. Chiang, M.-C. Chiang, and C.-S. Yang, “A Hyper-Heuristic Scheduling Algorithm for Cloud,” IEEE Trans. Cloud Comput., vol. PP, no. 99, pp. 1–1, 2014.
  40. I. Attiya, X. Zhang, and X. Yang, “TCSA : A Dynamic Job Scheduling Algorithm for Computational Grids,” in 2016 First IEEE International Conference on Computer Communication and the Internet (ICCCI), 2016, pp. 408–412.
  41. J. A. Torkestani, J. Akbari Torkestani, and J. A. Torkestani, “A new approach to the job scheduling problem in computational grids,” Cluster Comput., vol. 15, no. 3, pp. 201–210, 2011.
  42. S.-S. Kim et al., “Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization,” Soft Comput., vol. 17, no. 5, pp. 867–882, 2013.
  43. A. Y. S. Lam and V. O. K. Li, “Chemical Reaction Optimization for Task Scheduling in Grid Computing,” IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 10, pp. 1624–1631, 2011.
  44. Eberhart and Yuhui Shi, “Particle swarm optimization: developments, applications and resources,” in Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2001, vol. 1, pp. 81–86.
  45. Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 1999, vol. 3, pp. 1945–1950.
  46. J. Kennedy and R. Mendes, “Population structure and particle swarm performance.pdf,” Proc. 2002 Congr. Evol. Comput. CEC 2002, pp. 1671–1676, 2002.
  47. H. Liu, A. Abraham, and A. E. Hassanien, “Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm,” Futur. Gener. Comput. Syst., vol. 26, no. 8, pp. 1336–1343, 2010.
  48. J. Kennedy, “The particle swarm: social adaptation of knowledge,” in Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC ’97), 1997, pp. 303–308.
  49. 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,” Softw. Pract. Exp., vol. 41, no. 1, pp. 23–50, Jan. 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud computing job scheduling makespan particle swarm optimization resource allocation.

Powered by PhDFocusTM