Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
International Journal of Network Management
Experiments with Scheduling Using Simulated Annealing in a Grid Environment
GRID '02 Proceedings of the Third International Workshop on Grid Computing
AI Application Programming
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
IEEE Internet Computing
A Genetic Algorithm Based Approach for Scheduling Decomposable Data Grid Applications
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
Mapping Service-Level Agreements in Distributed Applications
IEEE Internet Computing
A Framework for Resource Allocation in Grid Computing
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Introduction to grid computing with globus
Introduction to grid computing with globus
Ordinal optimization based approach to the optimal resource allocation of grid computing system
Mathematical and Computer Modelling: An International Journal
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Grid computing connects heterogeneous resources to achieve the illusion of being a single available entity. Charging for these resources based on demand is often referred to as utility computing, where resource providers lease computing power with varying costs based on processing speed. Consumers using this resource have time and cost constraints associated with each job they submit. Determining the optimal way to divide the job among the available resources with regard to the time and cost constraints is tasked to the Grid Resource Broker (GRB). The GRB must use an optimization algorithm that returns an accurate result in a timely manner. The genetic algorithm and the simulated annealing algorithm can both be used to achieve this goal, although simulated annealing outperforms the genetic algorithm for use by the GRB. Determining optimal values for the variables used in each algorithm is often achieved through trial and error, and success depends upon the solution domain of the problem.