Tracking transaction footprints for non-intrusive end-to-end monitoring

Existing transaction monitoring solutions are either platform-specific or rely on instrumentation techniques, which limit their applicability. Consequently, transaction monitoring in enterprise environments often involves the manual collation of information spread across a variety of infrastructure elements and applications, and is a time-consuming and labor-intensive task. To address this problem, we have developed an online, non-intrusive and platform-agnostic solution for transaction monitoring. The solution includes a transaction model discovery component that leverages historical system log files, containing transaction footprints and generates a model of the transaction in terms of valid sequence of steps that a transaction instance may execute and the expected footprint patterns at each step. The online monitoring system, in turn, takes in only (a) online system log files and (b) the transaction model, as inputs and generates a dynamic execution profile of ongoing transaction instances that allows their status to be tracked at individual and aggregate levels, even when transaction footprints do not necessarily carry correlating identifiers as those injected through instrumentation. In this paper, we describe the transaction model discovery and monitoring system including the architecture and algorithms, followed by results from an empirical study, ongoing work on run-time model validation and directions for future research.