Hybrid PSACGA Algorithm for Job Scheduling to Minimize Makespan in Heterogeneous Grids

  • Amit ChhabraEmail author
  • Oshin
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 11)


Grid provides a clear, coordinated, consistent and reliable computing medium to solve complex sequential and parallel applications through the use of idle CPU cycles. Scheduling optimizes the objective function(s) by mapping the parallel jobs to the available resources. Owing to the heterogeneity of resources in grid, scheduling associates with class of NP-hard problems due to which reaching optimal solution surpasses the time constraint. Metaheuristic algorithms take polynomial time to reach the near-optimal solutions for NP-hard problems. Major research issues in metaheuristic algorithms are solution quality and convergence speed that have been revised by using consolidation approach. This paper proposes a hybrid PSACGA algorithm that consolidate the features of Particle Swarm Optimization (PSO), Ant Colony optimization (ACO) and operators of Genetic algorithm to solve parallel job scheduling problem. Experimental results of the proposed technique are compared with existing deterministic and metaheuristic job scheduling algorithms. Experimental results have indicated that the proposed hybrid PSAGA algorithm provides better performance than existing contemporary algorithms.


Grid Job scheduling Ant colony optimization Particle swarm optimization and genetic algorithm 


  1. 1.
    Qureshi, M.B., Dehnavi, M.M., Min-Allah, N., Qureshi, M.S., Hussain, H., Rentifis, I., Tziritas, N., Loukopoulos, T., Khan, S.U., Xu, C.-Z., Zomaya, A.Y.: Survey on grid resource allocation mechanisms. J. Grid Comput., Springer, 12(2), 399–441 (2014)Google Scholar
  2. 2.
    Sharma, S., Chabbra, A., Sharma, S.: Comparative analysis of scheduling algorithms for grid computing. In: Advances in Computing, Communications and Informatics (ICACCI) Conference, pp. 349–354. IEEE (2015)Google Scholar
  3. 3.
    Kant Soni, V., Sharma, R., Mishra, M.K.: An analysis of various job scheduling strategies in grid computing. In: Signal Processing Systems (ICSPS), 2010 2nd International Conference, vol. 2, pp. 349–354. IEEE (2010)Google Scholar
  4. 4.
    Xhafa, F., Abraham, A.: Computational models and heuristic methods for grid scheduling problems. J. Futue. Gener. Comput. Syst. 26(4), 608–621. Elsevier (2010)Google Scholar
  5. 5.
    Kolodziej, J.: Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems. Springer, New York (2012)CrossRefGoogle Scholar
  6. 6.
    Babafemi, O., Sanjay, M., Adigun, M.: Towards developing grid-based portals for e-commerce on-demand services on a utility computing platform. J. Procedia 4(1), 81–87. Elsevier (2013)Google Scholar
  7. 7.
    Desell, T., Newberg, L.A., Magdon-Ismail, M., Szymanski, B.K., Thompson, W.: Finding protein binding sites using volunteer computing grids. In: Gaol, F.L., Nguyen, Q.V. (eds.) 2nd International Congress on Computer Applications and Computational Science, pp. 385–393. Springer, Heidelberg (2012)Google Scholar
  8. 8.
    Sukhija, N., Datta, A.K.: C-grid: enabling iRODS-based grid technology for community health research. In: Information Technology in Bio- and Medical Informatics, pp. 17–31. Springer (2013)Google Scholar
  9. 9.
    Alobaedy, M.M., Ku-Mahamud, K.R.: Scheduling jobs in computational grid using hybrid ACS and GA approach. In: International Conference on Computing, Communications and IT Applications, pp. 223–228. IEEE (2014)Google Scholar
  10. 10.
    Zapfel, G., Braune, R., Bogl, M.: Metaheuristic Search Concepts a Tutorial with Applications to Production and Logistics. Springer, Heidelberg (2010)Google Scholar
  11. 11.
    Xin-She, Yang.: Nature-Inspired Optimization Algorithms. Elsevier, Amsterdam (2014)Google Scholar
  12. 12.
    Tavares Neto, R.F., Godinho Filho, M.: Literature Review Regarding Ant Colony Optimization Applied to Scheduling Problems: Guidelines for Implementation and Directions for Future Research, 26(1), pp. 150–161, (2013). ElsevierGoogle Scholar
  13. 13.
    Dorigo, M., Birattari, M.: Ant colony optimization. Encyclopedia of machine learning. Springer (2010)Google Scholar
  14. 14.
    Kang, Q., He, H., Wang, H., Jiang C.: A novel discrete particle swarm optimization algorithm for jobscheduling in grids. In: Natural Computation, 2008. ICNC ‘08. Fourth International Conference, pp. 401–405 (2008)Google Scholar
  15. 15.
    Imran, M., Hashim, R., Khalid, N.E.A.: An overview of particle swarm optimization variants. Science Direct, pp. 491–496. Elsevier (2013)Google Scholar
  16. 16.
    Ariyasingha, I.D.I.D., Fernando, T.G.I.: Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem, vol. 23, pp. 11–26. Elsevier (2015)Google Scholar
  17. 17.
    Kousalya, K.: To improve ant algorithm’ s grid scheduling using local search. Int. J. Comput. Cogn. 7, 47–57 (2009)Google Scholar
  18. 18.
    Izakian, H., Ladani, B.T., Zamanifar, K., Abraham, A.: A novel particle swarm optimization approach for grid job scheduling. Inf. Syst. Technol. manage. Springer, 31, 100–109 (2009)Google Scholar
  19. 19.
    Wu, Z., Ni, Z., Gu, L., Liu, X.: A revised discrete particle swarm optimization for cloud workflow scheduling. In: Proceedings of 2010 International conference on Computer Intelligent Security (CIS), pp. 184–188. IEEE (2010)Google Scholar
  20. 20.
    Zhang, L., Chen, Y., Sun, R.: A task scheduling algorithm based on PSO for grid computing. Intern. J. Comput. Intell. Res. 4, 37–43 (2008)Google Scholar
  21. 21.
    Beegom, A.S.A., Rajasree, M.S.: A particle swarm optimization based pareto optimal task scheduling in cloud computing. In: Advance in Swarm Intelligence Notes on Computer Science, p. 79–86. Springer (2014) Google Scholar
  22. 22.
    Hara, A., Matsushima, S., Ichimura, T., Takahama, T.: Ant colony optimization using exploratory ants for constructing partial solutions. In: Evolutionary Computation (CEC), 2010 IEEE Congress, pp. 1–7. IEEE (2010)Google Scholar
  23. 23.
    Arnaout, J.P., Rabadi, G., Musa, R.: A Two-Stage Ant Colony Optimization Algorithm to Minimize the Makespan on Unrelated Parallel Machines with Sequence-Dependent Setup Times, 21(6), 693–701. Springer (2010Google Scholar
  24. 24.
    Rais, H.M., Othman, Z.A., Hamdan, A.R.: Improved dynamic ant colony system (DACS) on symmetric traveling salesman problem (TSP). Published in Intelligent and Advanced Systems, ICIAS, pp. 43–48. IEEEGoogle Scholar
  25. 25.
    Anitha, J., Karpagam, M.: Ant colony optimization using pheromone updating strategy to solve job shop scheduling. In: 7th International Conference on Intelligent Systems and Control (ISCO), pp. 367–372. IEEE (2013)Google Scholar
  26. 26.
    Zhao, N., Wu, Z., Zhao, Y., Quan, T.: Ant colony optimization algorithm with mutation mechanism and its applications. Expert Syst. Appl., Elsevier, 37(7), 4805–4810 (2010)Google Scholar
  27. 27.
    Ku-Mahamud, K.R.: Ant Colony algorithm for job scheduling in grid computing. In: 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, Sintok, Malaysia, pp. 40–45, 26–28 May 2010Google Scholar
  28. 28.
    Tawfeek, M.A., El-Sisi, A., Keshk, A.E., Torkey, F.A.: Cloud task scheduling based on ant colony optimization. In: 8th International Conference on Computer Engineering and systems, p. 64–69 (2013)Google Scholar
  29. 29.
    Bagherzadeh, J., MadadyarAdeh, M.: An improved ant algorithm for grid scheduling problem using biased initial ants. In: 3rd International Conference on Computer devices, p. 373–378 (2011)Google Scholar
  30. 30.
    Liu, A.L.A., Wang, Z.W.Z.: Grid task scheduling based on adaptive ant colony algorithm. In: International Conference Management e-Commerce e-Government. p. 415–418. IEEE (2008)Google Scholar
  31. 31.
    Laalaoui, Y., Drias, H., Bouridah, A., Ahmed, R.B.: Ant colony system with stagnation avoidance for the scheduling of real-time tasks. In: Computational Intelligence in Scheduling, pp 1–6. IEEE (2009)Google Scholar
  32. 32.
    Xhafa, F., Duran, B., Kolodziej, J.: On exploitation vs exploration of solution space for grid scheduling. In: 3rd International Conference on Intelligent Networking and Collaborative Systems, pp. 164–171. IEEE (2011)Google Scholar
  33. 33.
    Alobaedy, M.M., Ku-Mahamud, K.R.: Scheduling jobs in computational grid using hybrid ACS and GA approach. In: International Conference on Computing, Communications and IT Applications, pp. 223–228. IEEE (2014) Google Scholar
  34. 34.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Network, pp. 1948–1995 (1995)Google Scholar
  35. 35.
    Xue, S., Wu, W.: Scheduling workflow in cloud computing basedon hybrid particle swarm algorithm. TELKOMNIKA Indones. J. Electr. Eng. 10, 1560–1566 (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Engineering and TechnologyGuru Nanak Dev UniversityAmritsarIndia

Personalised recommendations