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A hybrid heuristic resource allocation model for computational grid for optimal energy usage

机译:计算网格的混合启发式资源分配模型,用于优化能源使用

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摘要

>Computational grid helps in faster execution of compute intensive jobs. The resource allocation for the job execution in computational grid demands a lot of characteristic parameters to be optimised but in the process the green aspect is ignored. Reducing the energy consumption in computational grid is a major recent issue among researchers. The conventional systems, which offer energy efficient scheduling strategies, ignore other quality of service parameters while scheduling the jobs. The proposed work tries to optimise the energy for resource allocation and at the same time makes no compromise on other related characteristic parameters. A hybrid model, that uses genetic algorithm and graph theory concept has been proposed for this purpose. In this model, an energy saving mechanism is implemented using a dynamic threshold method followed by genetic algorithm to further consolidate the saving. Eventually, a graph theory concept of Minimum Spanning Tree (MST) is applied. The performance of the proposed model has been studied by its simulation. The result reveals the benefits achieved with the proposed model for optimal energy with resource allocation in the grid.
机译:>计算网格有助于更快地执行计算密集型作业。计算网格中用于执行作业的资源分配需要优化许多特征参数,但是在此过程中,绿色方面被忽略了。减少计算网格中的能耗是研究人员最近的一个主要问题。提供节能调度策略的常规系统在调度作业时会忽略其他服务质量参数。提出的工作试图优化用于资源分配的能量,同时在其他相关特征参数上不做任何妥协。为此,提出了使用遗传算法和图论概念的混合模型。在该模型中,采用动态阈值方法和遗传算法来实现节能机制,以进一步节省能源。最终,应用了最小生成树(MST)的图论概念。通过仿真研究了所提出模型的性能。结果揭示了该模型在网格中分配资源时获得最佳能源的好处。

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