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Motivating Dynamic Features for Resolution Time Estimation within IT Operations Management

Kayhan Moharreri, Jayashree Ramanathan, and Rajiv Ramnath (Department of Computer Science and Engineering, The Ohio State University)

Cloud-based services today depend on many layers of virtual technology and application services. Incidents and problems that arise in such complex operational environments are logged as a ticket, worked on by experts and finally resolved. To assist these experts, any machine recommendation method must meet the following critical business requirements: 1) the ticket must be resolved, meeting specific time constraints or Service Level Targets (SLTs), and 2) any predictive assistance must be trustworthy. Existing research uses probabilistic models to recommend transfers between experts based on limited features intrinsic to the ticket content, and does not demonstrate how to meet SLTs. To address this lack of research and ensure SLT-compliance for an incoming ticket given its recommended sequence of experts, there needs to be an accurate time-to-resolve (TTR) estimation. This research aims to identify important features for modeling time-to-resolve estimation given the routing recommendation sequences. This work particularly makes the following contributions: 1) constructs a framework for assessing TTR estimations and their SLT-compliance, 2) applies the assessment to a baseline estimation model to identify the need for better TTR modeling, and 3) uses language modeling to study the impact of anomalous content on the estimation error, and 4) introduces a set of dynamic features, and a methodology to rigorously model the TTR estimation.

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