Lei Chen
Key Lab of Computer Vision and Intelligent Information System, Chongqing University of Arts and Sciences, Yongchuan Chongqing, China
ABSTRACT
Resource scheduling is a extremely complex problem in grid computing. The performance of Grid strongly depends on the efficiency of resource scheduling. But, resource scheduling needs more optimal resource scheduling algorithm, otherwise it increases the cost and completion time and decreases the resource utilization efficiency. To improve performance of grid computing and resource utilization efficiency, it is urgent to design an optimal resource scheduling algorithm. This paper proposes Grid resource scheduling algorithm based on ant colony optimization. The proposed algorithm can achieve optimized resource allocation policy based on the users demand and improve the systems performance. The new resource scheduling algorithm is implemented and its advantages are investigated in the Gridsim simulator. The simulation results show that the application of resource scheduling algorithm based on ant colony optimization can effectively reduce total task completion time, balance the load of system well and improve the efficiency of resource scheduling.
PDF References Citation
How to cite this article
Lei Chen, 2013. Resource Scheduling Based on Ant Colony Optimization Algorithm in Grid Computing Environments. Information Technology Journal, 12: 8010-8014.
DOI: 10.3923/itj.2013.8010.8014
URL: https://scialert.net/abstract/?doi=itj.2013.8010.8014
DOI: 10.3923/itj.2013.8010.8014
URL: https://scialert.net/abstract/?doi=itj.2013.8010.8014
REFERENCES
- Chang, R.S., J.S. Chang and P.S. Lin, 2009. An ant algorithm for balanced job scheduling in grids. Future Generat. Comput. Syst., 25: 20-27.
CrossRefDirect Link - Chen, D., G. Chang, X. Zheng, D. Sun, J. Li and X. Wang, 2011. A novel P2P based grid resource discovery model. J. Networks, 6: 1390-1397.
CrossRefDirect Link - Dai, W., S. Liu and S. Liang, 2009. An improved ant colony optimization cluster algorithm based on swarm intelligence. J. Software, 4: 299-306.
Direct Link - Goyal, S.K. and M. Singh, 2012. Adaptive and dynamic load balancing in grid using ant colony optimization. Int. J. Eng. Technol., 4: 167-174.
Direct Link - Kokilavani, T. and D.I.G. Amalarethinam, 2012. Memory constrained ant colony system for task scheduling in grid computing. Int. J. Grid Comput. Appl., 3: 11-20.
Direct Link - Liang, K., L. Bai and X. Qu, 2011. Expectation value calculation of grid QoS parameters based on algorithm prim. J. Networks, 6: 1618-1624.
CrossRefDirect Link - Lorpunmanee, S., M.N. Sap, A.H. Abdullah and C. Chompoo-inwai, 2007. An ant colony optimization for dynamic job scheduling in grid environment. Int. J. Comput. Inform. Sci. Eng., 1: 207-214.
Direct Link - Nasir, H.J.A., K.R. Ku-Mahamud and A.M. Din, 2010. Load balancing using enhanced ant algorithm in grid computing. Proceedings of the 2nd International Conference Computational Intelligence, Modeling and Simulation, September 28-30, 2010, Bali, pp: 160-165.
CrossRefDirect Link - Qu, M.C., X.H. Wu and X.Z. Yang, 2011. A comprehensive optimization model based on time and cost constraints for resource selection in data grid. J. Software, 6: 2472-2478.
CrossRefDirect Link - Xie, G., T. Cao, C. Yan and Z. Wu, 2010. Texture features extraction of chest HRCT images based on granular computing. J. Multimedia, 5: 639-647.
CrossRefDirect Link