Fault isolation and event correlation for integrated fault management
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Formal Concept Analysis: Mathematical Foundations
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Introduction to Modern Information Retrieval
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Recovery Oriented Computing (ROC): Motivation, Definition, Techniques,
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Clio grows up: from research prototype to industrial tool
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Using Google distance to weight approximate ontology matches
Proceedings of the 16th international conference on World Wide Web
Why do internet services fail, and what can be done about it?
USITS'03 Proceedings of the 4th conference on USENIX Symposium on Internet Technologies and Systems - Volume 4
The Google Similarity Distance
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Beauty and the beast: the theory and practice of information integration
ICDT'07 Proceedings of the 11th international conference on Database Theory
Reuse frequency as metric for dependency resolver selection
CD'05 Proceedings of the Third international working conference on Component Deployment
High speed and robust event correlation
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Managing the configuration complexity of distributed applications in Internet data centers
IEEE Communications Magazine
An empirical study on configuration errors in commercial and open source systems
SOSP '11 Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles
EnCore: exploiting system environment and correlation information for misconfiguration detection
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Multi-tiered enterprise applications, which comprise of many (heterogeneous and possibly loosely coupled) components at various layers of the IT stack, typically have configuration dependencies between these different components. A change in the value of a configuration parameter of a component may require changing one or more parameters of other components to the same value. Keeping track of these dependencies is not a mean task as they are typically cross product and requires in-depth product/system knowledge. Our aim in this paper is to introduce techniques that do not require a system administrator to have in-depth knowledge about a multi-tiered system in order to be aware of the possible configuration dependencies in a system. Our focus in this paper does not include, for example, the performance-related dependencies such as the one that exists between the maximum number of threads and session timeout in an Apache server. In this paper we provide a method for analyzing existing deployments (that are functioning properly) to infer the configuration dependencies in a probabilistic sense. Firstly, we analyze the values of the configuration parameters of the various components to determine a candidate list of dependencies which includes both true and false ones. We then provide a heuristic that makes use of the statistics on the parameter name and value strings, computed from the configuration data and also the information on the web, to compute a rank for a dependency to be true. We provide a rank-ordered list of dependencies so that administrators can consult it to quickly and systematically identify the true dependencies. To complement our proposed approach we have performed an extensive study and experimentation to show the efficacy of our technique on data from two real-world solutions.