Guided Problem Diagnosis through Active Learning

There is widespread interest today in developing tools that can diagnose the cause of a system failure accurately and efficiently based on monitoring data collected from the system. Over time, the system monitoring data will contain two types of failure data: (i) annotated failure data L, which is monitoring data collected from failure states of the system, where the cause of failure has been diagnosed and attached as annotations with the data; and (ii) unannotated failure data U. Previous work on wholly- or partially-automated diagnosis focused on L or U in isolation. In this paper, we argue that it is important to consider both L and U together to improve the overall accuracy of diagnosis; and in particular, to proactively move instances from U to L. However, such movement requires manual diagnosis effort from system administrators. Since manual diagnosis is expensive and time-consuming, we propose an algorithm to make the best use of manual effort while maximizing the benefit gai...

• Type: Conference
• Year: 2008
• Where: ICAC
• Authors: Songyun Duan, Shivnath Babu
• Keywords: Autonomic Computing, Failure Data, Failure Data L, ICAC 2008, Unannotated Failure Data