ACSOS 2021
Mon 27 September - Fri 1 October 2021 Washington, DC, United States

Causal inference (CI) is one of the popular performance diagnosis methods, which infers the anomaly propagation from the observed data for locating the root causes. Although some specific CI methods have been employed, the overall performance of this class of methods on microservice performance diagnosis is not well understood. To this end, we select six representative CI methods from three categories and evaluate their performance against the challenges of microservice operations, including the large-scale observable data, heterogeneous anomaly symptoms, and a wide range of root causes. Our experimental results show that 1) CI techniques must be integrated with anomaly detection or anomaly scores to differentiate the causality in normal and abnormal data; 2) CI techniques are more robust to false positives in anomaly detection than knowledge-based non-CI method; 3) To get the fine-grained root causes, an effective way with CI techniques is to identify the faulty service first and infer the detailed explanation of the service abnormality. Overall, this work broadens the understanding of how CI methods perform on microservice performance diagnosis and provides recommendations for an efficient application of CI methods.

Wed 29 Sep

Displayed time zone: Eastern Time (US & Canada) change

11:45 - 12:50
Cross-disciplinary researchMain Track at AUDITORIUM 2
Chair(s): Alessandro Vittorio Papadopoulos Mälardalen University
Timing configurations affect the macro-properties of multi-scale feedback systems
Main Track
Patricia Mellodge University of Hartford, Ada Diaconescu LTCI Lab, Telecom Paris, Institute Politechnqie de Paris, Louisa Jane Di Felice Universidad Autónoma de Barcelona
Causal Inference Techniques for Microservice Performance Diagnosis: Evaluation and Guiding Recommendations
Main Track
Li Wu Elastisys AB/Technische Universität Berlin, Johan Tordsson Elastisys AB, Erik  Elmroth  Elastisys AB/Umea University, Odej  Kao Technische Universität Berlin
A Meta Reinforcement Learning-based Approach for Self-Adaptive System
Main Track
Mingyue Zhang Peking University, China, Jialong Li Waseda University, Japan, Haiyan Zhao Peking University, Kenji Tei Waseda University / National Institute of Informatics, Japan, Shinichi Honiden Waseda University / National Institute of Informatics, Japan, Zhi Jin Peking University