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Self managing monitoring for highly elastic large scale Cloud deployments

机译:自我管理监控,以实现高度弹性的大规模云部署

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摘要

Infrastructure as a Service computing exhibits a number of properties, which are not found in conventional server deployments. Elasticity is among the most significant of these properties which has wide reaching implications for applications deployed in cloud hosted VMs. Among the applications affected by elasticity is monitoring. In this paper we investigate the challenges of monitoring large cloud deployments and how these challenges differ from previous monitoring problems. In order to meet these unique challenges we propose Varanus, a highly scalable monitoring tool resistant to the effects of rapid elasticity. This tool breaks with many of the conventions of previous monitoring systems and leverages a multi-tier P2P architecture in order to achieve in situ monitoring without the need for dedicated monitoring infrastructure. We then evaluate Varanus against current monitoring architectures. We find that conventional monitoring tools perform acceptably for small, non changing cloud deployments. However in the case of large or highly elastic deployments current tools perform unacceptably incurring increased latencies, high load and slowed operation necessitating that a new, alternative tool be used. Further, we demonstrate that Varanus maintains low latency and low resource monitoring state propagation at scale and during during periods of high elasticity.
机译:基础架构即服务计算具有许多属性,而常规服务器部署中没有这些属性。弹性是这些属性中最重要的属性之一,对于部署在云托管VM中的应用程序具有广泛的影响。受到弹性影响的应用程序包括监视。在本文中,我们调查了监视大型云部署的挑战以及这些挑战与以前的监视问题有何不同。为了应对这些独特的挑战,我们提出了Varanus,这是一种可扩展的监视工具,可抵抗快速弹性的影响。该工具打破了以前的监视系统的许多常规,并利用了多层P2P架构,以实现现场监视,而无需专用的监视基础结构。然后,我们根据当前的监视体系结构评估Varanus。我们发现,常规的监视工具对于小型,不变的云部署可以令人满意地执行。但是,在部署大型或高弹性的情况下,当前的工具执行不可接受的操作会导致延迟增加,高负载以及操作速度变慢,因此必须使用新的替代工具。此外,我们证明Varanus在规模和高弹性期间保持低延迟和低资源监视状态传播。

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