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Grid computing utilizes distributed heterogeneous resources to support large-scale or complicated computing tasks, and an appropriate resource scheduling algorithm is fundamentally important for the success of grid applications. Due to the complex and dynamic properties of grid environments, traditional model-based methods may result in poor scheduling performance in practice. In this paper, the authors propose a heuristic genetic load balancing algorithm. The implementation and simulation results indicate that their approaches can allocate jobs efficiently and effectively.
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