Probabilistic fault localization in communication systems using belief networks
IEEE/ACM Transactions on Networking (TON)
Failure Diagnosis Using Decision Trees
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Shrink: a tool for failure diagnosis in IP networks
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
A Hybrid Rule-Based/Case-Based Reasoning Approach for Service Fault Diagnosis
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 02
Using temporal correlation for fault localization in dynamically changing networks
International Journal of Network Management
Assisting failure diagnosis through filesystem instrumentation
Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research
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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.