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Adaptive divisible load scheduling strategies for workstation clusters with unknown network resources

机译:网络资源未知的工作站集群的自适应可分割负载调度策略

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Conventional divisible load scheduling algorithms attempt to achieve optimal partitioning of massive loads to be distributed among processors in a distributed computing system in the presence of communication delays in the network. However, these algorithms depend strongly upon the assumption of prior knowledge of network parameters and cannot handle variations or lack of information about these parameters. In this paper, we present an adaptive strategy that estimates network parameter values using a probing technique and use them to obtain optimal load partitioning. Three algorithms, based on the same strategy, are presented in the paper, incorporating the ability to cope with unknown network parameters. Several illustrative numerical examples are given. Finally, we implement the adaptive algorithms on an actual network of processor nodes using MPI implementation and demonstrate the feasibility of the adaptive approach.
机译:常规的可分割负载调度算法试图在网络中存在通信延迟的情况下实现要在分布式计算系统中的处理器之间分配的海量负载的最佳划分。但是,这些算法在很大程度上取决于对网络参数的先验知识的假设,无法处理有关这些参数的变化或信息不足。在本文中,我们提出了一种自适应策略,该策略使用探测技术估算网络参数值,并使用它们来获得最佳负载分配。本文提出了基于相同策略的三种算法,其中包括应对未知网络参数的能力。给出了几个说明性的数值示例。最后,我们使用MPI实现在处理器节点的实际网络上实现自适应算法,并证明了自适应方法的可行性。

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