Abstract
Grid scheduling is characterized as the way toward settling on planning choices including resources over multiple administrative domains. This procedure can search through different administrative areas to utilize a particular machine or scheduling one job for exhausting various resources at a particular node or multiple nodes. From a grid point of view, a job is anything that needs a resource. The primary objective of grid is to give service with high dependability and minimal effort for substantial volumes of clients and support teamwork. In this paper, we consider a directed acyclic graph (DAG) with nodes and edges where the nodes are considered the task and the edges specify the order of execution of the tasks as a grid. This kind of problem is called the precedence-constrained problem. The selective breeding algorithm is an efficient algorithm to solve NP-hard problems. One such example of NP-hard problem is the precedence-constrained problem. So we consider SBA algorithm to solve precedence-constrained problems and found optimal solution of 13 units when compared with the traditional methods of 23 units. And it is also proved that the amount of waiting time is reduced greatly when compared to the traditional methods. So by implementing SBA for the grid scheduling problem more time is saved and is proved to be efficient.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Foster, I., Kesselman, C.: Computational Grids Blueprint for a New Computing Infrastructure, pp. 15–52. Morgan Kaufmann, San Francisco, CA (1999)
Foster, I., Kesselman, C., Salisbury, C., Tuecke, S.: The data grid: towards an architecture for the distributed management and analysis of large scientific data sets. J. Netw. Comput. Appl. 23(3), 187–200 (2001)
Jin, H., Zheng, R., Zhang, Q., Li, Y.: Components and workflow based grid programming environment for integrated image-processing applications. In: Concurrency and Computation: Practice and Experience, vol. 18, no. 14. Wiley Ltd., pp. 1857–1869 (2006)
Yagoubi, B., Meddeber, M.: A load balancing model for grid environment. In: Proceeding of 22nd international symposium on computer and information sciences (ISCISC 2007), pp. 1–7 (2007)
Ni, L., Zhang, J., Yan, C., Jiang. C.: Heuristic algorithm for task scheduling based on mean load. In: First international conference on semantics, knowledge and grid (SKG’05), Beijing, China 27–29 Nov 2009
Shah, H: A low-complexity task scheduling algorithm for heterogeneous computing systems. In: Asia Modeling Symposium (AMS’09), Indonesia (2009)
Ramya, R., Thomas, S.: An optimal job scheduling algorithm in computational grids. In: The international conference on communication, computing and information technology (ICCCMIT), pp. 12–16 (2012)
Keerthika, P., Kasthuri, N.: An efficient grid scheduling algorithm with fault tolerance and user satisfaction. Math. Prob. Eng. 2013(Article ID 340294), 1–9 (2013)
Chauhan, P., Nitin: Decentralized scheduling algorithm for DAG based tasks on P2P grid. J. Eng. 2014(Article IDÂ 202843), 14 pages (2014)
Zhu, L., Su, Z., Guo, W., Jina, Y., Suna, W., Hua, W.: Dynamic multi DAG scheduling algorithm for optical grid environment. In: Network Architectures, Management, and Applications V, Proc. of SPIE, vol. 6784, 67841F (2007)
Sriramya, P., Parvathavarthini, B., Balamurugan, T.: A novel evolutionary selective breeding algorithm and its application. Asian J. Sci. Res. 6, 107–114 (2013)
Sriramya, P., Parvathavarthini, B.: Performance analysis of selective breeding algorithm on one dimensional bin packing problems. J. Inst. Eng. 93, 255–258 (2013)
Galinier, P., Hao, J.-K.: Hybrid evolutionary algorithms for graph coloring. J. Comb. Optim. 3, 379–397 (1999)
Bidgoli, A.M., Nezad, Z.M.: A new scheduling algorithm design for grid computing tasks. In: 5 Symposium on Advances in science and Technology, Iran, pp. 21–30 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sriramya, P., Karthika, R. (2020). Solving Grid Scheduling Problems Using Selective Breeding Algorithm. In: Luhach, A., Kosa, J., Poonia, R., Gao, XZ., Singh, D. (eds) First International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-15-0029-9_46
Download citation
DOI: https://doi.org/10.1007/978-981-15-0029-9_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0028-2
Online ISBN: 978-981-15-0029-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)