Monitoring multi-tier clustered systems with invariant metric relationships

To ensure high availability, self-managing systems require self-monitoring and a system model against which to ana-lyze monitoring data. Characterizing relationships between system metrics has been shown to model simple multi-tier transaction systems effectively, enabling failure detection and fault diagnosis. In this paper we show how to extend this invariant metric-relationships approach to clustered multi-tier systems. We show through analysis and experimenta-tion that näıve application of the approach increases cost dramatically while reducing diagnosis accuracy. We demon-strate that randomization at the load balancer during the invariant-identification phase will improve diagnosis accu-racy, though it neither completely eliminates the problem nor reduces the cost; indeed, it may increase the cost, as this approach will require a long learning phase to remove all accidental correlations. Finally, we argue that knowing the system structure is necessary to effectively apply invari-ants to the clustered environment