Abstract
This paper provides an overview of grid computing, grid scheduling and grid scheduling algorithms. In this paper various study and comparison of Min-Min algorithm and IACO algorithm has been described. The problem to make full use of all types of resources in the grid tasks scheduling has been discussed in this paper. Ant Colony Algorithm in grid computing is described. The paper discusses the problem to make full use of all types of resources in the grid tasks scheduling. When a large number of tasks request the grid resources, according to the task type of the adoption of appropriate strategies, the different tasks are assigned to the appropriate resources nodes to run it, in order to achieve the optimum utilization of resources. As the heterogeneous and dynamic of grid environment, while the different requirements for applications, making the tasks scheduling become extremely complex, the algorithm will have a direct impact on task execution efficiency of the grid environment, as well as the success or failure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Foster, I., Kesselman, C.: The anatomy of the grid. Int. J. Supercomput. Appl. 1–25 (2001)
Zhu, Y., Wei, Q.: An Improved Ant Colony Algorithm for Independent Tasks Scheduling of Grid, vol. 2, pp. 566–569. IEEE (2010)
Chang, R.S., Chang, J.S., Lin, P.S.: Balanced job assignment based on ant algorithm for computing grids. In: Asia-Pacific Services Computing Conference, pp. 291–295 (2007)
Chen, M.: Toward adaptive ant colony algorithm. In: International Conference on Measuring Technology and Mechatronics Automation, pp. 1035–1038 (2010)
Lorpunmanee, S., Sap, M.N., Abdullah, A.H., Inwai, C.C.: An ant colony optimization for dynamic job scheduling in grid environment. World Academy of Science Engineering and Technology, pp. 314–321
Braun, T.D., Siegel, H. J., Beck, N., Boloni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hensgen, D., Freund, R.F.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61(6), 810–837 (2001)
Dorigo, M., Stutzle, T.: The ant colony optimization metaheuristic: Algorithms, applications and advances. In: International Series in Operations Research and Management Science, vol. 57, pp. 251–285 (2002)
Fidanova, S., Durchova, M.: Ant Algorithm for Grid Scheduling Problem, pp. 405–412. Springer (2006)
Ritchie, G., Levine, J:. A hybrid ant algorithm for scheduling independent jobs in heterogeneous environments. J. Am. Associat. Artif. Intell. 178–184 (2004)
Kousalya, K., Balasubramanie, P.: To improve ant algorithm for grid scheduling using local search. Int. J. Comput. Cogn. 7(4), 47–57
Yan, H., Shen, X.Q., Li, X., Wu, M.H.: An improved ant algorithm for job scheduling in grid computing. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, pp. 2957–2961 (2005)
Buyya, R., Murshed, M.: GridSim: a toolkit for the modeling, and simulation of distributed resource management, and scheduling for grid computing. Concurr. Computat. Pract. Exper. 14, 1175–1220 (2002)
Bai, L., Hu, Y., Lao, S., Zhang, W.: Task scheduling with load balancing using multiple ant colonies optimization in grid computing. In: Sixth International Conference on Natural Computation (ICNC), pp. 2715–2719 (2010)
Tang, B., Yin, Y., Liu, Q., Zhou, Z.: Research on the application of ant colony algorithm in grid resource scheduling. Wirel. Commun. Netw. Mobile Comput. 1–4 (2008)
Author, F.: Contribution title. In: 9th International Proceedings on Proceedings, pp. 1–2. Publisher, Location (2010)
LNCS Homepage. http://www.springer.com/lncs. Last accessed 21 Nov 2016
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Vir, R., Vasudeva, R., Sharma, V., Sandeep (2019). Optimised Scheduling Algorithms and Techniques in Grid Computing. In: Benavente-Peces, C., Slama, S., Zafar, B. (eds) Proceedings of the 1st International Conference on Smart Innovation, Ergonomics and Applied Human Factors (SEAHF). SEAHF 2019. Smart Innovation, Systems and Technologies, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-030-22964-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-22964-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22963-4
Online ISBN: 978-3-030-22964-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)