Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Wed 13 Nov 2019 11:00 - 11:20 at Hillcrest - Cloud and Online Services Chair(s): Dan Hao

Large-scale online systems are complex, fast-evolving, and hardly bug-free despite the testing efforts. Backend system monitoring cannot detect many types of issues, such as UI related bugs, bugs with small impact on backend system indicators, or errors from third-party co-operating systems, etc. However, users are good informers of such issues: They will provide their feedback for any types of issues. This experience paper discusses our design of iFeedback, a tool to perform real-time issue detection based on user feedback texts. Unlike traditional approaches that analyze user feedback with computation-intensive natural language processing algorithms, iFeedback is focusing on fast issue detection, which can serve as a system life-condition monitor. In particular, iFeedback extracts word combination-based indicators from feedback texts. This allows iFeedback to perform fast system anomaly detection with sophisticated machine learning algorithms. iFeedback then further summarizes the texts with an aim to effectively present the anomaly to the developers for root cause analysis. We present our representative experiences in successfully applying iFeedback in tens of large-scale production online service systems in ten months.

Wed 13 Nov

10:40 - 12:20: Papers - Cloud and Online Services at Hillcrest
Chair(s): Dan HaoPeking University
ase-2019-papers10:40 - 11:00
Understanding Exception-Related Bugs in Large-Scale Cloud Systems
Haicheng ChenThe Ohio State University, Wensheng DouInstitute of Software, Chinese Academy of Sciences, Yanyan JiangNanjing University, Feng QinOhio State University, USA
ase-2019-papers11:00 - 11:20
iFeedback: Exploiting User Feedback for Real-time Issue Detection in Large-Scale Online Service Systems
Wujie ZhengTencent, Inc., Haochuan LuFudan University, Yangfan ZhouFudan University, Jianming LiangTencent, Haibing ZhengTencent, Yuetang DengTencent, Inc.
ase-2019-Journal-First-Presentations11:20 - 11:40
Software Microbenchmarking in the Cloud. How Bad is it Really?
Christoph LaaberUniversity of Zurich, Joel ScheunerChalmers | University of Gothenburg, Philipp LeitnerChalmers University of Technology & University of Gothenburg
Link to publication Pre-print
ase-2019-papers11:40 - 12:00
Continuous Incident Triage for Large-Scale Online Service Systems
Junjie ChenTianjin University, Xiaoting HeMicrosoft, Qingwei LinMicrosoft Research, China, Hongyu ZhangThe University of Newcastle, Dan HaoPeking University, Feng GaoMicrosoft, Zhangwei XuMicrosoft, Yingnong DangMicrosoft Azure, Dongmei ZhangMicrosoft Research, China
ase-2019-Demonstrations12:00 - 12:10
Kotless: a Serverless Framework for Kotlin
Vladislav TankovJetBrains, ITMO University, Yaroslav GolubevJetBrains Research, ITMO University, Timofey BryksinJetBrains Research, Saint-Petersburg State University
ase-2019-Demonstrations12:10 - 12:20
FogWorkflowSim: An Automated Simulation Toolkit for Workflow Performance Evaluation in Fog Computing
Xiao LiuSchool of Information Technology, Deakin University, Lingmin FanSchool of Computer Science and Technology, Anhui University, Jia XuSchool of Computer Science and Technology, Anhui University, Xuejun LiSchool of Computer Science and Technology, Anhui University, Lina GongSchool of Computer Science and Technology, Anhui University, John GrundyMonash University, Yun YangSwinburne University of Technology