Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
Xhafa, F., Abraham, A.: Computational models and heuristic methods for grid scheduling problems. J. Futue. Gener. Comput. Syst. 26(4), 608–621. Elsevier (2010)
Kolodziej, J.: Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems. Springer, New York (2012)
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)
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)
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)
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)
Zapfel, G., Braune, R., Bogl, M.: Metaheuristic Search Concepts a Tutorial with Applications to Production and Logistics. Springer, Heidelberg (2010)
Xin-She, Yang.: Nature-Inspired Optimization Algorithms. Elsevier, Amsterdam (2014)
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). Elsevier
Dorigo, M., Birattari, M.: Ant colony optimization. Encyclopedia of machine learning. Springer (2010)
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)
Imran, M., Hashim, R., Khalid, N.E.A.: An overview of particle swarm optimization variants. Science Direct, pp. 491–496. Elsevier (2013)
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)
Kousalya, K.: To improve ant algorithm’ s grid scheduling using local search. Int. J. Comput. Cogn. 7, 47–57 (2009)
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)
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)
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)
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)
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)
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 (2010
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. IEEE
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)
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)
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 2010
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)
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)
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)
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)
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)
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)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Network, pp. 1948–1995 (1995)
Xue, S., Wu, W.: Scheduling workflow in cloud computing basedon hybrid particle swarm algorithm. TELKOMNIKA Indones. J. Electr. Eng. 10, 1560–1566 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chhabra, A., Oshin (2018). Hybrid PSACGA Algorithm for Job Scheduling to Minimize Makespan in Heterogeneous Grids. In: Bhattacharyya, S., Sen, S., Dutta, M., Biswas, P., Chattopadhyay, H. (eds) Industry Interactive Innovations in Science, Engineering and Technology . Lecture Notes in Networks and Systems, vol 11. Springer, Singapore. https://doi.org/10.1007/978-981-10-3953-9_12
Download citation
DOI: https://doi.org/10.1007/978-981-10-3953-9_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3952-2
Online ISBN: 978-981-10-3953-9
eBook Packages: EngineeringEngineering (R0)