OVIS: a tool for intelligent, real-time monitoring of computational clusters
OVIS: a tool for intelligent, real-time monitoring of computational clusters
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OVIS：智能实时监控计算集群的工具
DOI：
10.1109/ipdps.2006.1639698
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
P. Pébay
中科院分区：
文献类型：
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作者：
J. Brandt;A. Gentile;D. Hale;P. Pébay
传统的集群监控方法考虑单例节点，使用制造商指定的极限作为故障“预测”的阈值。我们开发了一种工具 OVIS，用于监控和分析大型计算平台，该平台使用统计方法来表征大量统计相似设备的单个设备行为。 OVIS 的基线功能包括可视化显示有关状态变量（例如温度、CPU 利用率、风扇速度）的确定性信息及其汇总统计数据。将集群可视化地视为一个比较整体，而不是单个节点，是调整集群配置和确定实时更改的影响的一种简单且有用的方法。此外，OVIS 还采用了一种新颖的贝叶斯推理方案，可以动态推断系统正常行为的模型，并确定系统中显示的值的概率范围。在当前适用模型下不太可能出现的单个节点值被标记为异常。这可能是比等待跨越某个阈值更早的问题指标，该阈值必须设置得很高以防止过多的误报。我们介绍 OVIS 并讨论其在集群配置和环境调整以及生产集群中的异常和问题发现中的应用
Traditional cluster monitoring approaches consider nodes in singleton, using manufacturer-specified extreme limits as thresholds for failure "prediction". We have developed a tool, OVIS, for monitoring and analysis of large computational platforms which, instead, uses a statistical approach to characterize single device behaviors from those of a large number of statistically similar devices. Baseline capabilities of OVIS include the visual display of deterministic information about state variables (e.g., temperature, CPU utilization, fan speed) and their aggregate statistics. Visual consideration of the cluster as a comparative ensemble, rather than as singleton nodes, is an easy and useful method for tuning cluster configuration and determining effects of realtime changes. Additionally, OVIS incorporates a novel Bayesian inference scheme to dynamically infer models for the normal behavior of a system and to determine bounds on the probability of values evinced in the system. Individual node values that are unlikely given the current applicable model are flagged as aberrant. This can be a much earlier indicator of problems than waiting for the crossing of some threshold that is necessarily set high to preclude too many false alarms. We present OVIS and discuss its applications in cluster configuration and environmental tuning and to abnormality and problem discovery in our production clusters