pyp2pcluster: A cluster discovery tool

R. Tracey, Mobayode O. Akinsolu, V. Elisseev, Sultan Shoaib
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Abstract

It is becoming increasingly common for laboratories and universities to share computing resources. Also as cloud usage and applications continue to expand, a hybrid cloud working model is fast becoming a common standard practice. In line with these present-day trends, we present in this paper an open-source Python library that provides information on high performance computing (HPC) clusters and systems that are available to a user via a peer to peer (P2P) infrastructure. These metrics include the size of system and availability of nodes, along with the speed of connection between clusters. We will present the benefits of using a P2P model compared to traditional client server models and look at the ease in which this can be implemented. We will also look at the benefits and uses of gathering this data in one location in order to assist with the managing of complex workloads in heterogeneous environments.
pyp2pcluster:集群发现工具
实验室和大学共享计算资源正变得越来越普遍。此外,随着云使用和应用程序的不断扩展,混合云工作模型正迅速成为一种常见的标准实践。根据这些当前的趋势,我们在本文中提出了一个开源Python库,它提供了关于高性能计算(HPC)集群和系统的信息,这些信息可以通过点对点(P2P)基础设施提供给用户。这些指标包括系统的大小和节点的可用性,以及集群之间的连接速度。我们将介绍与传统的客户机-服务器模型相比,使用P2P模型的好处,并分析实现P2P模型的难易程度。我们还将研究在一个位置收集这些数据的好处和用途,以便帮助管理异构环境中的复杂工作负载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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