Advertisement

TETS: A Genetic-Based Scheduler in Cloud Computing to Decrease Energy and Makespan

  • Mohammad Shojafar
  • Maryam Kardgar
  • Ali Asghar Rahmani Hosseinabadi
  • Shahab Shamshirband
  • Ajith Abraham
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 420)

Abstract

In Cloud computing environments, computing resources are available for users, and they only pay for used resources The most important issues in cloud computing are scheduling and energy consumption which many researchers worked on them. In these systems a scheduling mechanism has two phases: task prioritization and processor selection. Different priorities may cause to different makespan and for each processor which assigned to the task, the energy consumption is different. So a good scheduling algorithm must assign priority to each task and select the best processor for them, in such a way that makespan and energy consumption be minimized. In this paper, we proposed a two phase’s algorithm for scheduling, named TETS, the first phase is task prioritization and the second phase is processor assignment. We use three prioritization methods for prioritize the tasks and produce optimized initial chromosomes and assign the tasks to processors which is an energy-aware model. Simulation results indicate that our algorithm is better than previous algorithms in terms of energy consumption and makespan. It can improve the energy consumption by 20 % and makespan by 4 %.

References

  1. 1.
    Shojafar, M., Javanmardi, S., Abolfazli, S., Cordeschi, F.: Fuge: a joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method. Cluster Comput. 18(2), 829–844 (2015)Google Scholar
  2. 2.
    Jadeja, Y., Modi, K.: Cloud computing-concepts, architecture and challenges. In: Computing, Electronics and Electrical Technologies (ICCEET), 2012 International Conference on, pp. 877–880. IEEE (2012)Google Scholar
  3. 3.
    Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y.C., Talbi, E.-G, Zomaya, A.Y., Tuyttens, D.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J. Parallel Distribut. Comput. 71(11), 1497–1508 (2011)Google Scholar
  4. 4.
    Shojafar, M., Cordeschi, N., Amendola, D., Baccarelli, E,: Energy-saving adaptive computing and traffic engineering for real-time-service data centers. In: International Conference on Communications, 2015. ICC’15, pp. 9866–9872. IEEE (2015)Google Scholar
  5. 5.
    Hajj, H., El-Hajj, W., Dabbagh, M., Arabi, T.R.: An algorithm-centric energy-aware design methodology. Very Large Scale Integr. (VLSI) Syst. IEEE Trans. 22(11), 2431–2435 (2014)Google Scholar
  6. 6.
    Lee, Y.C., Zomaya, A.Y.: Minimizing energy consumption for precedence-constrained applications using dynamic voltage scaling. In: CCGRID’09, pp. 92–99. IEEE (2009)Google Scholar
  7. 7.
    Papagianni, C., Leivadeas, A., Papavassiliou, S., Maglaris, V., Cervello-Pastor, C., Monje, A.: On the optimal allocation of virtual resources in cloud computing networks. Comput. IEEE Transa. 62(6), 1060–1071 (2013)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gutierrez-Garcia, J.O., Sim, K.M.: A family of heuristics for agent-based elastic cloud bag-of-tasks concurrent scheduling. Future Gener. Comput. Syst. 29(7), 1682–1699 (2013)Google Scholar
  9. 9.
    Chiang, R.C., Huang, H.H.: Tracon: interference-aware scheduling for data-intensive applications in virtualized environments. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, p. 47. ACM (2011)Google Scholar
  10. 10.
    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)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Li, J., Peng, J., Lei, Z., Zhang, W.: An energy-efficient scheduling approach based on private clouds. J. Inf. Comput. Sci. 8(4), 716–724 (2011)Google Scholar
  12. 12.
    Garg, S.K., Yeo, C.S., Anandasivam, A., Buyya, R.: Energy-efficient scheduling of hpc applications in cloud computing environments. arXiv preprint arXiv:0909.1146 (2009)
  13. 13.
    Shojafar, M., Pooranian, Z., Abawajy, J.H., Meybodi, M.R.: An efficient scheduling method for grid systems based on a hierarchical stochastic petri net. J. Comput. Sci. Eng. 7(1), 44–52 (2013)Google Scholar
  14. 14.
    Raduca, E., Adrian, P., Raduca, M., Drugarin, C.A., Silviu, D., Rudolf, C.: The algorithm for going through a labyrinth by an autonomous. In: Ingenieria Informatica, pp. 1–4 (2015)Google Scholar
  15. 15.
    Anghel, C.V., Dorica, S.M., Silviu, D.: Method for programming an autonomous vehicle using pic 16f877 microcontroller. In: Information and Communication Technologies International Conference-ICTIC 2014, vol. 3, pp. 317–320 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mohammad Shojafar
    • 1
  • Maryam Kardgar
    • 2
  • Ali Asghar Rahmani Hosseinabadi
    • 2
  • Shahab Shamshirband
    • 3
  • Ajith Abraham
    • 4
  1. 1.DIET DepartmentSapienza University of RomeRomeItaly
  2. 2.Young Research Club, Behshahr BranchIslamic Azad UniversityBehshahrIran
  3. 3.Computer System and Technology DepartmentUniversity of MalayaKLMalaysia
  4. 4.MIR LabsScientific Network for Innovation and Research ExcellenceAuburnUSA

Personalised recommendations