A Simulation Study of Multi-criteria Scheduling in Grid Based on Genetic Algorithms

Job scheduling is a critical mechanism which can affect significantly the performance of complex heterogeneous distributed systems, such as Grids. Since, computational Grids are multi-criteria environments by nature, modern allocation algorithms should concern more than one criterion to produce scheduling solutions. In this paper, we propose a multi-criteria Genetic Algorithm, which tries to eliminate the security risks and power consumption of the system, apart from the completion time of jobs. Although Genetic Algorithms are suitable for large search space problems such as job scheduling, they are too slow to be executed online. Hence, we changed the implementation of a traditional genetic algorithm, introducing the Accelerated Genetic Algorithm. In addition, Accelerated Genetic Algorithm with Overhead is also presented, which concerns the extra overhead caused by the application of Accelerated Genetic Algorithm. We propose the best combination of weights of each criterion of our Genetic Algorithm in order to maximize the performance of the system. Afterwards, Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead are compared with three well-known heuristics. Simulation results indicate a substantial performance advantage of both Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead.