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Monitoring High Performance Networks in Large-scale Clusters

机译:监视大型集群中的高性能网络

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The number of large-scale clusters is rising. They are included into grids or become key components of large structures. As more users and projects rely on RFC clusters, high availability and security are requirements for a fast growing adoption and use. In this paper, we, focus on high performance networks. All HPC clusters are built on top of them. We demonstrate that classical instrumentations are inefficient in HPC environment, they do not scale or cause a significant loss of performance. Based on this fact, we highlight clusters properties; nodes have assigned roles and are coupled at various levels. Moreover, we study the main characteristics of resource usage for each type of node and propose an instrumentation that can be effectively deployed. It results in fine-grained mechanisms adapted to system architecture, and performance constraints. Relevant information is collected over time. Two properties are verified online and dynamically: coherency and containment. Each induces a type of verification and both aim at reducing recovery time from failure and security risk of a whole cluster. We illustrate our methodology on QsNet by K. Magontis et al. (2001) network and provide a way to increase safety of high performance networks and clusters
机译:大型集群的数量正在增加。它们包含在网格中或成为大型结构的关键组件。随着越来越多的用户和项目依赖RFC群集,高可用性和安全性是快速增长的采用和使用的要求。在本文中,我们将重点放在高性能网络上。所有HPC群集都建立在它们之上。我们证明了传统仪器在HPC环境中效率低下,它们无法扩展规模或不会导致性能显着下降。基于这一事实,我们突出显示了群集的属性。节点已分配角色,并在各个级别上耦合。此外,我们研究了每种类型节点的资源使用的主要特征,并提出了可以有效部署的工具。这会导致适应系统体系结构和性能约束的细粒度机制。相关信息会随着时间的推移而收集。在线和动态验证了两个属性:一致性和包含性。每种方法都会引发一种验证,并且两者都旨在减少从整个群集的故障和安全风险中恢复的时间。我们通过K. Magontis等人在QsNet上说明了我们的方法。 (2001年)网络,并提供一种提高高性能网络和群集安全性的方法

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