Mining activity data for dynamic dependency discovery in e-business systems

The growing popularity of e-business has stimulated web sites to evolve from static content servers to complex multi-tier systems built from heterogeneous server platforms. E-businesses now spend a large fraction of their IT budgets maintaining, troubleshooting, and optimizing these web sites. It has been shown that such system management activities may be simplified or automated to various extents if a dynamic dependency graph of the system were available. Currently, all known solutions to the dynamic dependency graph extraction problem are intrusive in nature, i.e. require modifications at application or middleware level. In this paper, we describe non-intrusive techniques based on data mining, which process existing monitoring data generated by server platforms to automatically extract the system component dependency graphs in multi-tier e-business platforms, without any additional application or system modification.