AISTAR: An Intelligent System for Online IT Ticket Automation Recommendation

• Published in: 2018 IEEE International Conference on Big Data (Big Data) pp. 1875 - 1884
• Main Authors: Wang, Qing, Zeng, Chunqiu, Iyengar, S. S., Li, Tao, Shwartz, Larisa, Grabarnik, Genady Ya
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2018
• Subjects: Automation; Business; Cognitive IT service management; Cold-start problem; Engines; Interactive recommender system; Monitoring; Multi-armed bandit model; Recommender systems; Taxonomy
• DOI: 10.1109/BigData.2018.8622446

An efficient delivery of IT services for increasingly complex IT environments demands an intelligent automated solution for resolving existing and potential issues. An automation recommender system, promptly suggesting the most proper scripted resolution to an arriving IT incident ticket, would play a significant role in IT automation services. Hence, developing a comprehensive framework supporting becomes imperative for continuous improvement of automation recommendation.In this paper, we first identify the challenges of IT services followed by a discussion on AISTAR (an intelligent system for online IT ticket automation recommendation) designed and developed to provide them. Specifically, we define and formalize automation recommendation procedure as a multi-armed bandit problem with dependent arms, which is capable of achieving the optimal tradeoff between exploitation of the system for the best automation recommendation and exploration of automation execution information for future recommendation. Two novel multi-armed bandit models are proposed and integrated to handle the aforementioned challenges in IT automation services. Empirical studies on a large ticket dataset from IBM Global Services demonstrate both the effectiveness and efficiency of our intelligent integrated system. AISTAR is earmarked for Cognitive Event Automation for IBM Service delivery.