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Mapping Algorithms Optimizing the Overall Manhattan Distance for Pre-Occupied Cluster Computers in SLA-Based Grid Environments

机译:基于SLA的网格环境中映射算法优化了被占领集群计算机的总体曼哈顿距离

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Grid applications are more and more widely used nowadays. One of the major challenges is to provide a reliable and predictable platform for computations of various kinds. In order to overcome this challenge, Grid management systems such as the virtual resource manager (VRM) implement scheduling and mapping algorithms at level of the local management systems with support for resource reservation in advance. In this paper, we examine three different mapping algorithms for supercomputers and cluster systems with respect to execution time and the achieve able performance regarding important metrics such as overall Manhattan distance and achievable utilization. The results show the importance of carefully implementing scheduling and mapping algorithms in Grid environments.
机译:如今,网格应用越来越广泛地被使用。主要挑战之一是为各种计算提供可靠且可预测的平台。为了克服这一挑战,诸如虚拟资源管理器(VRM)之类的Grid管理系统在本地管理系统级别实施了调度和映射算法,并预先支持资源预留。在本文中,我们针对执行时间以及有关重要度量(例如总体曼哈顿距离和可达到的利用率)的可实现性能,研究了针对超级计算机和集群系统的三种不同映射算法。结果表明,在网格环境中仔细实现调度和映射算法的重要性。

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