DocumentCode :
2911038
Title :
Task scheduling by Mean Field Annealing algorithm in grid computing
Author :
Xue, Guixiang ; Zhao, Zheng ; Ma, Maode ; Su, Tonghua ; Zhang, Tianwen ; Liu, Shuang
Author_Institution :
Sch. of Comput. Sci. & Technol., Tianjin Univ., Tianjin
fYear :
2008
fDate :
1-6 June 2008
Firstpage :
783
Lastpage :
787
Abstract :
Desirable goals for grid task scheduling algorithms would shorten average delay, maximize system utilization and fulfill user constraints. In this work, an agent-based grid management infrastructure coupled with mean field annealing (MFA) scheduling algorithm has been proposed. An agent in grid utilizes a neural network algorithm to manage and schedule tasks. The Hopfield neural network is good at finding optimal solution with multi-constraints and can be fast to converge to the result. However, it is often trapped in a local minimum. Stochastic simulated annealing algorithm has an advantage in finding the optimal solution and escaping from the local minimum. Both significant characteristics of Hopfield neural network structure and stochastic simulated annealing algorithm are combined together to yield a mean field annealing scheme. A modified cooling procedure to accelerate reaching equilibrium for normalized mean field annealing has been applied to this scheme. The simulation results show that the scheduling algorithm of MFA works effectively.
Keywords :
Hopfield neural nets; grid computing; scheduling; simulated annealing; stochastic processes; Hopfield neural network; agent-based grid management; grid computing; mean field annealing algorithm; stochastic simulated annealing; task scheduling; Computer network management; Cooling; Delay systems; Grid computing; Hopfield neural networks; Neural networks; Processor scheduling; Scheduling algorithm; Simulated annealing; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
Type :
conf
DOI :
10.1109/CEC.2008.4630885
Filename :
4630885
Link To Document :
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