Efficient Customer Incident Triage via Linking with System Incidents
In cloud service systems, customers will report the service issues they have encountered to cloud service providers. Despite many issues can be handled by the support team, sometimes the customer issues can not be easily solved, thus raising customer incidents. Quick troubleshooting of a customer incident is critical. To this end, a customer incident should be assigned to its responsible team accurately in a timely manner.
Our industrial experiences show that linking customer incidents with detected system incidents can help the customer incident triage. In particular, our empirical study on 7 real cloud service systems shows that with the additional information about the system incidents (i.e., incident reports generated by system monitors), the triage time of customer incidents can be accelerated 13.1$\times$ on average. Based on this observation, in this paper, we propose LinkCM, a learning based approach to automatically link customer incidents to monitor reported system incidents. LinkCM incorporates a novel learning-based model that effectively extracts related information from two resources, and a transfer learning strategy is proposed to help LinkCM achieve better performance without huge amount of data. The experimental results indicate that LinkCM is able to achieve accurate link prediction. Furthermore, case studies are presented to demonstrate how LinkCM can help the customer incident triage procedure in real production cloud service systems.
Wed 11 NovDisplayed time zone: (UTC) Coordinated Universal Time change
01:00 - 01:30 | Cloud / Services 1Industry Papers / Research Papers / Paper Presentations / Tool Demos at Virtual room 2 | ||
01:00 2mTalk | Beware the Evolving ‘Intelligent’ Web Service! An Integration Architecture Tactic to Guard AI-First Components Research Papers Alex Cummaudo Deakin University, Australia, Scott Barnett Deakin University, Australia, Rajesh Vasa Deakin University, Australia, John Grundy Monash University, Australia, Mohamed Abdelrazek Deakin University, Australia DOI | ||
01:03 1mTalk | Efficient Customer Incident Triage via Linking with System Incidents Industry Papers Jiazhen Gu Fudan University, China, Jiaqi Wen Peking University, China, Zijian Wang Fudan University, China, Pu Zhao Microsoft Research, China, Chuan Luo Microsoft Research, China, Yu Kang Microsoft Research, China, Yangfan Zhou Fudan University, China, Li Yang Microsoft Azure, USA, Jeffrey Sun Microsoft Azure, USA, Zhangwei Xu Microsoft, China, Bo Qiao Microsoft Research, China, Liqun Li Microsoft Research, China, Qingwei Lin Microsoft Research, China, Dongmei Zhang Microsoft Research, China DOI | ||
01:05 1mTalk | How to Mitigate the Incident? An Effective Troubleshooting Guide Recommendation Technique for Online Service Systems Industry Papers Jiajun Jiang Tianjin University, China, Weihai Lu Peking University, China, Junjie Chen Tianjin University, China, Qingwei Lin Microsoft Research, China, Pu Zhao Microsoft Research, China, Yu Kang Microsoft Research, China, Hongyu Zhang University of Newcastle, Australia, Yingfei Xiong Peking University, Feng Gao Microsoft, China, Zhangwei Xu Microsoft, China, Yingnong Dang Microsoft, USA, Dongmei Zhang Microsoft Research, China DOI | ||
01:07 1mTalk | Identifying Linked Incidents in Large-Scale Online Service Systems Research Papers Yujun Chen Microsoft Research, China, Xian Yang Hong Kong Baptist University, China, Hang Dong Microsoft Research, China, Xiaoting He Chinese Academy of Sciences, China, Hongyu Zhang University of Newcastle, Australia, Qingwei Lin Microsoft Research, China, Junjie Chen Tianjin University, China, Pu Zhao Microsoft Research, China, Yu Kang Microsoft Research, China, Feng Gao Microsoft, China, Zhangwei Xu Microsoft, China, Dongmei Zhang Microsoft Research, China DOI | ||
01:09 1mTalk | Mono2Micro: An AI-Based Toolchain for Evolving Monolithic Enterprise Applications to a Microservice Architecture Tool Demos Anup K. Kalia IBM Research, USA, Jin Xiao IBM Research, USA, Chen Lin IBM Research, USA, Saurabh Sinha IBM Research, John Rofrano IBM Research, USA, Maja Vukovic IBM Research, USA, Debasish Banerjee IBM, n.n. DOI | ||
01:11 1mTalk | Threshy: Supporting Safe Usage of Intelligent Web Services Tool Demos Alex Cummaudo Deakin University, Australia, Scott Barnett Deakin University, Australia, Rajesh Vasa Deakin University, Australia, John Grundy Monash University, Australia DOI | ||
01:13 1mTalk | Towards Intelligent Incident Management: Why We Need It and How We Make It Industry Papers Zhuangbin Chen Chinese University of Hong Kong, China, Yu Kang Microsoft Research, China, Liqun Li Microsoft Research, China, Xu Zhang Microsoft Research, China, Hongyu Zhang University of Newcastle, Australia, Hui Xu Fudan University, China, Yangfan Zhou Fudan University, China, Li Yang Microsoft Azure, USA, Jeffrey Sun Microsoft Azure, USA, Zhangwei Xu Microsoft, China, Yingnong Dang Microsoft, USA, Feng Gao Microsoft, China, Pu Zhao Microsoft Research, China, Bo Qiao Microsoft Research, China, Qingwei Lin Microsoft Research, China, Dongmei Zhang Microsoft Research, China, Michael Lyu CUHK DOI Media Attached File Attached | ||
01:15 15mTalk | Conversations on Cloud / Services 1 Paper Presentations Alex Cummaudo Deakin University, Australia, Anup K. Kalia IBM Research, USA, Jiajun Jiang Tianjin University, China, Zhuangbin Chen Chinese University of Hong Kong, China, M: Satish Chandra Facebook, USA | ||