DRACA: Decision support for root cause analysis and change impact analysis for CMDBs

As business services become increasingly dependent on information technology (IT), it also becomes increasingly important to maximize the decision support for managing IT. Configuration Management Data Bases (CMDBs) store fundamental information about IT systems, such as the system's hardware, software and services. This information can help provide decision support for root cause analysis and change impact analysis. We have worked with our industrial research partner, CA, and with CA customers to identify challenges to the use of CMDBs to semi-automatically solve these problems. In this paper we propose a framework called DRACA (Decision Support for Root Cause Analysis and Change Impact Analysis). This framework mines key facts from the CMDB and in a sequence of three steps combines these facts with incident reports, change reports and expert knowledge, along with temporal information, to construct a probabilistic causality graph. Root causes are predicted and ranked by probabilistically tracing causality edges backwards from incidents to likely causes. Conversely, change impacts can be predicted and ranked by tracing from a proposed change forward along causality edges to locate likely undesirable impacts.