A self-improving helpdesk service system using case-based reasoning techniques
Computers in Industry
Automating ITSM Incident Management Process
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
Illuminating trouble tickets with sublanguage theory
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Adaptive diagnosis in distributed systems
IEEE Transactions on Neural Networks
Who do you call? problem resolution through social compute units
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
| Hi-index | 0.01 |
In recent years, IT Service Management (ITSM) has become one of the most researched areas of IT. Incident and Problem Management are two of the Service Operation processes in the IT Infrastructure Library (ITIL). These two processes aim to recognize, log, isolate and correct errors which occur in the environment and disrupt the delivery of services. Incident Management and Problem Management form the basis of the tooling provided by an Incident Ticket Systems (ITS). In an ITS system, seemingly unrelated tickets created by end users and monitoring systems can coexist and have the same root cause. The connection between failed resource and malfunctioning services is not realized automatically, but often established manually by means of human intervention. This need for human involvement reduces productivity. The introduction of automation would increase productivity and therefore reduce the cost of incident resolution. In this paper, we propose a model to correlate incident tickets based on three criteria. First, we employ a category-based correlation that relies on matching service identifiers with associated resource identifiers, using similarity rules. Secondly, we correlate the configuration items which are critical to the failed service with the earlier identified resource tickets in order to optimize the topological comparison. Finally, we augment scheduled resource data collection with constraint adaptive probing to minimize the correlation interval for temporally correlated tickets. We present experimental data in support of our proposed correlation model.