Look who's talking: discovering dependencies between virtual machines using CPU utilization
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
LogSig: generating system events from raw textual logs
Proceedings of the 20th ACM international conference on Information and knowledge management
Root cause detection in a service-oriented architecture
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
An integrated framework for optimizing automatic monitoring systems in large IT infrastructures
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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With the growing complexity in computer systems, it has been a real challenge to detect and diagnose problems in today's large-scale distributed systems. Usually, the correlations between measurements collected across the distributed system contain rich information about the system behaviors, and thus a reasonable model to describe such correlations is crucially important in detecting and locating system problems. In this paper, we propose a transition probability model based on markov properties to characterize pair-wise measurement correlations. The proposed method can discover both the spatial (across system measurements) and temporal (across observation time) correlations, and thus such a model can successfully represent the system normal profiles. Problem determination and localization under this framework is fast and convenient. The framework is general enough to discover any types of correlations (e.g. linear or non-linear). Also, model updating, system problem detection and diagnosis can be conducted effectively and efficiently. Experimental results show that, the proposed method can detect the anomalous events and locate the problematic sources by analyzing the real monitoring data collected from three companies' infrastructures.