Abstract
Work load and resource management are two important factors that have to manage across the grid environment. To increase the overall efficiency of grid based infrastructure the work load across the grid environment has to manage. Hence the work load must be evenly scheduled across the grid nodes so that grid resources can be properly exploited. The technique that we have investigated in this paper is based upon the combination of genetic algorithms which is an evolutionary algorithm and artificial neural networks. Both of these techniques are applied for local grid load balancing. Genetic algorithm selects the optimal set of jobs for assigning to the grid nodes which overall minimizes the total execution time. Afterwards when optimal set of jobs is selected they are assigned to artificial neural network which selects the minimum loaded grid processor for further processing of this optimal set of jobs. We compare our proposed technique with the already existing strategies for load balancing like random algorithm, round robin algorithm, decreasing time algorithm and least connection algorithm. Results shows that our strategy gives optimal results in terms of overall time efficiency. So we can overall conclude that GA’s and ANN’s increase overall efficiency of job scheduling especially in case where the tasks coming for scheduling and processing nodes are continuously increasing.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yen, N.Y., Kuo, S.Y.F.: An intergrated approach for internet resources mining and searching. J. Converg. 3, 37–44 (2012)
Pyshkin, E., Kuznetsov, A., Approaches for web search user interfaces: How to improve the search quality for various types of information. J. Converg. 1, 1–8 (2010)
Ai Ling, A.P., Masao, M.: Selection of model in developing information security criteria for smart grid security system. J. Converg. 20, 39–46 (2011)
Schmid, O., Hirsbrunner, B.: PerComw middleware for distributed collaborative ad-hoc environments. In: IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 435–438, (2012)
Aikebaier,A., Enokido, T., Takizawa, M.: Trustworthy group making algorithm in distributed systems. Hum. centric Comput. Inf. Sci. 1, 6. doi:10.1186/2192-1962-1-6,:22 (2011)
Maheshwari, P.: A dynamic load balancing algorithm for a heterogeneous computing environment. In: HICSS 29th Hawaii International Conference on System Sciences (HICSS’96) Volume 1: Software Technology and Architecture, p. 338 (1996)
Yagoubi, B., Slimani, Y.: Task load balancing strategy for grid computing. J. Comput. Sci. Science Publications, USA, vol. 3 (2007)
Zomaya, A.Y.: Observations on using genetic algorithms for dynamic load-balancing. Parallel Distrib. Syst. IEEE Trans. 12(9), 899–911 (2001)
Nikravan, M., Kashani, M.H.: A genetic algorithm for process scheduling in distributed operating systems considering load balancing. J. Parallel Distrib. Comput.70(1), 1–6 Netherlands
Yuan, J., Ding, S., Wang, C.: Tasks scheduling based on neural networks. Grid Conf. Nat. Comput. 3, 372–376
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Inam, N.T., Daud Awan, M., Afzal, S.S. (2012). Load Balancing in Grid Computing Using AI Techniques. In: Yeo, SS., Pan, Y., Lee, Y., Chang, H. (eds) Computer Science and its Applications. Lecture Notes in Electrical Engineering, vol 203. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5699-1_93
Download citation
DOI: https://doi.org/10.1007/978-94-007-5699-1_93
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5698-4
Online ISBN: 978-94-007-5699-1
eBook Packages: Computer ScienceComputer Science (R0)