Efficient string matching: an aid to bibliographic search
Communications of the ACM
The Vision of Autonomic Computing
Computer
Towards an Autonomic Computing Environment
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
Generic Adapter Logging Toolkit
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Symptom Database Builder for Autonomic Computing
ICAS '06 Proceedings of the International Conference on Autonomic and Autonomous Systems
Gridifying IBM's Generic Log Adapter to Speed-Up the Processing of Log Data
CISIS '07 Proceedings of the First International Conference on Complex, Intelligent and Software Intensive Systems
Ibm websphere v5.0 performance, scalability, and high availability websphere handbook series
Ibm websphere v5.0 performance, scalability, and high availability websphere handbook series
Stream-based event prediction using bayesian and bloom filters
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
High volumes of event stream indexing and efficient multi-keyword searching for cloud monitoring
Future Generation Computer Systems
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Today's enterprise applications can produce vast amounts of information both during system testing and in production. Correlation of this information can be difficult as it is generally stored in a range of different event logs, the format of which can be application or vendor specific. Furthermore these large logs can be physically distributed across a number of different locations. As a result it can be difficult to form a coherent understanding of the overall system behaviour. This has implications for a number of domains (e.g. autonomic computing, system testers), where an understanding of the system behaviour at run-time is required (e.g. for problem determination, autonomic management etc.) This paper presents an approach and implementation of run-time correlation of large volumes of log data and symptom matching of known issues in the context of large enterprise applications. Our solution provides for automatic data collection, data normalisation into a common format, run-time correlation and analysis of the data to give a coherent view of system behaviour at run-time and a symptom matching mechanism that can identify known errors in the correlated data on the fly.