Self managing monitoring for highly elastic large scale cloud deployments
Self managing monitoring for highly elastic large scale cloud deployments
复制标题
高弹性大规模云部署的自我管理监控
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
A. Barker
中科院分区：
文献类型：
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作者：
Jonathan Stuart Ward;A. Barker
基础设施即服务计算表现出许多在传统服务器部署中找不到的属性。弹性是其中最重要的属性之一，它对部署在云托管的VM中的应用程序具有广泛的影响。受弹性影响的应用之一是监控。
在本白皮书中，我们调查了监控大型云部署的挑战，以及这些挑战与之前的监控问题有何不同。为了应对这些独特的挑战，我们提出了Varanus，这是一个高度可扩展的监控工具，可以抵抗快速弹性的影响。该工具打破了以前监控系统的许多常规，并利用多层P2P架构来实现现场监控，而不需要专用的监控基础设施。
然后，我们对照当前的监控架构对Varanus进行评估。我们发现，对于小型、不变的云部署，传统监控工具的性能还可以接受。然而，在大型或高度弹性部署的情况下，当前工具的执行效果令人无法接受，从而导致延迟增加、高负载和运行速度变慢，因此必须使用新的替代工具。进一步，我们证明了Varanus在规模和高弹性期间保持低延迟和低资源监控状态传播。
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.