KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Effective Task Scheduling and Dynamic Resource Optimization based on Heuristic Algorithms in Cloud Computing Environment

  • NZanywayingoma, Frederic (Department of Computer Science and Communication Engineering, University of Science and Technology Beijing) ;
  • Yang, Yang (Department of Computer Science and Communication Engineering, University of Science and Technology Beijing)
  • Received : 2016.05.10
  • Accepted : 2017.08.09
  • Published : 2017.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.

Keywords

Acknowledgement

Supported by : National Science Foundation of China, Central Universities

References

  1. Qi, H. and A. Gani, "Research on mobile cloud computing: Review, trend and perspectives," in Proc. of Digital Information and Communication Technology and it's Applications (DICTAP), 2012 Second International Conference on, IEEE, 2012.
  2. Weng, C., et al., "The hybrid scheduling framework for virtual machine systems," in Proc. of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments, ACM, 2009.
  3. Mell, P. and T. Grance, The NIST definition of cloud computing, 2011. https://www.nist.gov/publications/nist-definition-cloud-computing
  4. Luo, Y. and B. Plale, "Hierarchical mapreduce programming model and scheduling algorithms," in Proc. of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), IEEE Computer Society, 2012.
  5. Zhan, S. and H. Huo, "Improved PSO-based task scheduling algorithm in cloud computing," Journal of Information & Computational Science, 9(13): p. 3821-3829, 2012.
  6. Ning, W., et al., "A task scheduling algorithm based on qos and complexity-aware optimization in cloud computing," in Proc. of Information and Communications Technology 2013, National Doctoral Academic Forum on, IET, 2013.
  7. Zhu, K., et al., "Hybrid genetic algorithm for cloud computing applications," in Proc. of Services Computing Conference (APSCC), 2011 IEEE Asia-Pacific, IEEE, 2011.
  8. Lin, S.-W., K.-C. Ying, and C.-Y. Huang, "Multiprocessor task scheduling in multistage hybrid flowshops: A hybrid artificial bee colony algorithm with bi-directional planning," Computers & Operations Research, 40(5), p. 1186-1195, 2013. https://doi.org/10.1016/j.cor.2012.12.014
  9. Al-maamari, A. and F.A. Omara, "Task Scheduling Using PSO Algorithm in Cloud Computing Environments," International Journal of Grid and Distributed Computing, 8(5): p. 245-256, 2015. https://doi.org/10.14257/ijgdc.2015.8.5.24
  10. Xu, S.-H., et al., "A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows," Sensors, 15(9), p. 21033-21053, 2015. https://doi.org/10.3390/s150921033
  11. Karaboga, D., Artificial bee colony algorithm. scholarpedia, 5(3), p. 6915, 2010. https://doi.org/10.4249/scholarpedia.6915
  12. Kumar, R.S. and S. Gunasekaran, "Improving task scheduling in large scale cloud computing environment using artificial bee colony algorithm," International Journal of Computer Applications, 103(5), 2014.
  13. Akkoyunlu, M.C., O. Engin, and K. Buyukozkan, "A harmony search algorithm for hybrid flow shop scheduling with multiprocessor task problems," in Proc. of Modeling, Simulation, and Applied Optimization (ICMSAO), 2015 6th International Conference on, IEEE, 2015.
  14. Mirjalili, S. and S.Z.M. Hashim, "A new hybrid PSOGSA algorithm for function optimization," in Proc. of Computer and information application (ICCIA), 2010 international conference on, IEEE, 2010.
  15. Lili, X., et al., "An improved binary PSO-based task scheduling algorithm in green cloud computing," in Proc. of 2014 9th International Conference on Communications and Networking in China, 126-31, 2014.
  16. Agrawal, S. and R. Shimpi, Modified Particle Swarm Optimization.
  17. Labed, S., A. Gherboudj, and S. Chikhi, "A modified hybrid particle swarm optimization algorithm for multidimensional knapsack problem," Int. J. Comput. Appl, 34(2): p. 1, 2011.
  18. Khan, S.U., et al., "A Modified Particle Swarm Optimization Algorithm for Global Optimizations of Inverse problems," IEEE Transactions on Magnetics, Vol. 52, Issue 3, 2016.
  19. Pandey, S., et al., "A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments," in Proc. of Advanced information networking and applications (AINA), 2010 24th IEEE international conference on, IEEE, 2010.
  20. Peyvandi, M., M. Zafarani, and E. Nasr, "Comparison of Particle Swarm Optimization and the genetic algorithm in the improvement of power system stability by an SSSC-based controller," Journal of Electrical Engineering and Technology, 6(2): p. 182-191, 2011. https://doi.org/10.5370/JEET.2011.6.2.182
  21. Sridhar, M., "Hybrid Genetic Swarm Scheduling for Cloud Computing," Global Journal of Computer Science and Technology, 15(3), 2015.
  22. Shi, P., et al., "Dependable Deployment Method for Multiple Applications in Cloud Services Delivery Network," [J]. China Communications, 8(4): p. 65-75, 2011.
  23. Guo, L., et al., "Task scheduling optimization in cloud computing based on heuristic algorithm," Journal of Networks, 7(3): p. 547-553, 2012.
  24. Jena, R., "Multi objective task scheduling in cloud environment using nested PSO framework," Procedia Computer Science, 57: p. 1219-1227, 2015. https://doi.org/10.1016/j.procs.2015.07.419
  25. Hsu, Y.-C., P. Liu, and J.-J. Wu, "Job sequence scheduling for cloud computing," in Proc. of Cloud and Service Computing (CSC), 2011 International Conference on, IEEE, 2011.
  26. Priyadarsini, R.J. and L. Arockiam, "PBCOPSO: A parallel optimization algorithm for task scheduling in cloud environment," Indian Journal of Science and Technology, 8(16), 2015.
  27. Eberhart, R.C. and Y. Shi, "Comparison between genetic algorithms and particle swarm optimization," in Proc. of International Conference on Evolutionary Programming, Springer, 1998.
  28. Hassan, R., et al., "A comparison of particle swarm optimization and the genetic algorithm," in Proc. of the 1st AIAA multidisciplinary design optimization specialist conference, 2005.
  29. Jones, K.O., "Comparison of genetic algorithm and particle swarm optimization," in Proc. of Int. Conf. Computer Systems and Technologies, 2005.
  30. Elbeltagi, E., T. Hegazy, and D. Grierson, "Comparison among five evolutionary-based optimization algorithms. Advanced engineering informatics," 19(1): p. 43-53, 2005. https://doi.org/10.1016/j.aei.2005.01.004
  31. De Jong, K.A. andW.M. Spears, "Using Genetic Algorithms to Solve NP-Complete Problems," in ICGA, 1989.
  32. Xu, L., et al., "An improved binary PSO-based task scheduling algorithm in green cloud computing," in Proc. of Communications and Networking in China (CHINACOM), 2014 9th International Conference on, IEEE, 2014.
  33. Pooranian, Z., et al., "An efficient meta-heuristic algorithm for grid computing," Journal of Combinatorial Optimization, 30(3): p. 413-434, 2015. https://doi.org/10.1007/s10878-013-9644-6
  34. Kennedy, J., "Particle swarm optimization," Encyclopedia of Machine Learning, Springer, p. 760-766, 2010.
  35. Eberhart, R. and J. Kennedy, "A new optimizer using particle swarm theory," in Proc. of Micro Machine and Human Science, 1995. MHS '95., Proceedings of the Sixth International Symposium on, 1995.
  36. Chapman, B., "When clouds become green: the green open cloud architecture," Parallel Computing: From Multicores and GPU's to Petascale, 19, p. 228, 2010.
  37. Sedighizadeh, M., et al., "Parameter optimization for a PEMFC model with particle swarm optimization," Int J Eng Appl Sci, 3, p. 102-108, 2011.
  38. Liu, C.-Y., C.-M. Zou, and P. Wu, "A task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing," in Proc. of Distributed Computing and Applications to Business, Engineering and Science (DCABES), 2014 13th International Symposium on, IEEE, 2014.
  39. Rostami, A. and M. Lashkari, "Extended PSO algorithm for improvement problems K-Means clustering algorithm," International Journal of Managing Information Technology, 6(3), p. 17, 2014. https://doi.org/10.5121/ijmit.2014.6302
  40. Calheiros, R.N., et al., "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms," Software - Practice and Experience, 41(1), p. 23-50, 2011. https://doi.org/10.1002/spe.995

Cited by

  1. Integrated MOPSO algorithms for task scheduling in cloud computing vol.36, pp.2, 2017, https://doi.org/10.3233/jifs-181005
  2. Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing vol.49, pp.9, 2017, https://doi.org/10.1007/s10489-019-01448-x