Towards Combining Online & Offline Management for Big Data Applications
Brian Laub, Chengwei Wang, Karsten Schwan, Chad Marcus Huneycutt · International Conference on Autonomic Computing · 2014
Traditional data center monitoring systems focus on collecting basic metrics such as CPU and memory usage, in a centralized location, giving administrators a summary of global system health via a database of observations. Conversely, emerging research systems are focusing on scalable, distributed monitoring capable of quickly detecting and alerting administrators to anomalies. This paper outlines VStore, a system that seeks to combine fast online anomaly detection with offline storage and analysis of monitoring data. VStore can be used as a historical reference to help guide administrators towards quickly classifying and fixing anomalous behavior once a problem has been detected. We demonstrate this idea with a distributed big streaming data application, and explore three common fault scenarios in this application. We show that each scenario exhibits a slightly different monitoring history, which may be undetectable by online algorithms that are resource-constrained. We also offer a discussion of how historical data captured by VStore can be combined with online monitoring tools to improve troubleshooting efforts in the data center.