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Grid Matrix: a grid simulation tool to focus on the propagation of resource and monitoring information

机译:Grid Matrix:网格仿真工具,专注于资源的传播和监视信息

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Grid Computing proposes unlimited access to different computational resources in a transparent way. High performance execution in grid environments is virtually impossible without timely access to accurate and up-to-date information related to distributed resources and services. Due to inherent difficulty of testing the different information propagation policies in real grid infrastructures, several simulation frameworks arose to help in this issue. In this work, we present Grid Matrix, an extension to one of the most used grid simulation tools (SimGrid2) to focus on the propagation of monitoring and resource information allowing the creation of virtual grid infrastructures. This extension enables GUI editing of network topology and provides the feature of scripting to define simulation details based on the newly developed C++ and Python bindings of SimGrid2 API. As a case study, Grid Matrix was used to test four different policies: hierarchical, super-peer, best-neighbor and random. The simulated scenario consisted of 96 master nodes based on the real Teragrid infrastructure as was publicly available at the time of writing this paper. We introduce three metrics that capture and summarize the information propagation behavior: LIR, GIR and GIV. LIR captures the local behavior quantifying the amount of up-to-date information in each node. GIR evaluates the global information state in the whole network averaging the LIR values, while GIV measures the variability of LIR. In the presented case, the best results in terms of the proposed metrics were attained by the hierarchical policy, followed by super-peer which outperformed random and best-neighbor. The modern and modular design of the scripting features included in Grid Matrix, in close conjunction with the user friendly GUI happened to be a very powerful tool for the evaluation of new propagation policies of resource information.
机译:网格计算建议以透明的方式无限制地访问不同的计算资源。如果不及时访问与分布式资源和服务有关的准确和最新信息,则在网格环境中执行高性能几乎是不可能的。由于在实际的网格基础架构中测试不同的信息传播策略固有的困难,因此出现了一些仿真框架来帮助解决此问题。在这项工作中,我们展示了Grid Matrix,它是最常用的网格仿真工具(SimGrid2)的扩展,以专注于监视和资源信息的传播,从而可以创建虚拟网格基础架构。此扩展允许对网络拓扑进行GUI编辑,并提供脚本功能来基于新开发的SimGrid2 API的C ++和Python绑定定义仿真详细信息。作为案例研究,网格矩阵用于测试四种不同的策略:分层策略,超对等策略,最佳邻居策略和随机策略。该模拟场景由96个主节点组成,这些节点基于真实的Teragrid基础结构,在撰写本文时已公开提供。我们引入了三个度量来捕获和总结信息传播行为:LIR,GIR和GIV。 LIR捕获量化每个节点中最新信息量的本地行为。 GIR会评估LIR值的平均值,以评估整个网络中的全局信息状态,而GIV会评估LIR的可变性。在所提出的情况下,通过提出的度量标准,可以通过分层策略获得最佳结果,其次是性能优于随机和最佳邻居的超级节点。 Grid Matrix中包含的脚本功能的现代化和模块化设计与用户友好的GUI紧密结合,是评估资源信息新传播策略的非常强大的工具。

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