With millions of customers accessing online service all over the world, ensuring high service availability is very critical for cloud system. In recent years, empowered by advanced data mining and machine learning technology, there emerges extensive study on data-driven solution to detect anomalous system behavior and diagnose the root cause. However, without any surveilance of data generation process, the existing passive data-driven approach may lead to biased analysis result induced by observed and unobserved confounding factors in the dynamic and heterogeneous system, and thus affect service availability with misleading mitigation actions. In this paper, we propose to infuse causal thinking to the current data-driven solution for cloud system. We developed CARE, a causal aware root cause discovery engine, which utilizes Random Control Trial to proactively generate less ambiguous data for further analysis. A case study shows the application of CARE to Microsoft Office365.

References

[1]

Chetan Bansal, Sundararajan Renganathan, Ashima Asudani, Olivier Midy, and Mathru Janakiraman. 2020. DeCaf: diagnosing and triaging performance issues in large-scale cloud services. Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice (2020).

Digital Library

Google Scholar

[2]

J. Chen, X. He, Q. Lin, Y. Xu, H. Zhang, D. Hao, F. Gao, Z. Xu, Y. Dang, and D. Zhang. 2019. An Empirical Investigation of Incident Triage for Online Service Systems. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). 111--120. https://doi.org/10.1109/ICSE-SEIP.2019.00020

Crossref

Google Scholar

[3]

Y. Dang, Q. Lin, and P. Huang. 2019. AIOps: Real-World Challenges and Research Innovations. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). 4-5. https://doi.org/10.1109/ICSE-Companion.2019.00023

Crossref

Google Scholar

[4]

Kamil Figiela, Adam Gajek, Adam Zima, Beata Obrok, and Maciej Malawski. 2018. Performance evaluation of heterogeneous cloud functions. Concurrency and Computation: Practice and Experience 30, 23 (2018).

Google Scholar

[5]

Ze Li, Qian Cheng, Ken Hsieh, Yingnong Dang, Peng Huang, Pankaj Singh, Xinsheng Yang, Qingwei Lin, Youjiang Wu, Sebastien Levy, and Murali Chintalapati. 2020. Gandalf: An Intelligent, End-To-End Analytics Service for Safe Deployment in Large-Scale Cloud Infrastructure. In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20). USENIX Association, Santa Clara, CA, 389--402. https://www.usenix.org/conference/nsdi20/presentation/li

Google Scholar

[6]

F. Lin, Keyur Muzumdar, N. Laptev, Mihai-Valentin Curelea, S. Lee, and S. Sankar. 2020. Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment. Proceedings of the ACM on Measurement and Analysis of Computing Systems 4 (2020), 1 - 23.

Digital Library

Google Scholar

[7]

Kamlesh Kumar Pandey and Diwakar Shukla. 2020. Stratified Sampling-Based Data Reduction and Categorization Model for Big Data Mining. In Communication and Intelligent Systems, Jagdish Chand Bansal, Mukesh Kumar Gupta, Harish Sharma, and Basant Agarwal (Eds.). Springer Singapore, Singapore, 107--122.

Google Scholar

[8]

Harald Stolberg, Geoffrey Norman, and Isabelle Trop. 2005. Randomized Controlled Trials. AJR. American journal of roentgenology 183 (01 2005), 1539--44. https://doi.org/10.2214/ajr.183.6.01831539

Crossref

Google Scholar

[9]

Elizabeth Stuart, Haiden Huskamp, Kenneth Duckworth, Jeffrey Simmons, Zirui Song, Michael Chernew, and Colleen Barry. 2014. Using propensity scores in difference-in-differences models to estimate the effects of a policy change. Health services outcomes research methodology 14(12 2014), 166--182. https://doi.org/10.1007/s10742-014-0123-z

Crossref

Google Scholar

[10]

Xu Zhang, Qingwei Lin, Yong Xu, Si Qin, Hongyu Zhang, Bo Qiao, Yingnong Dang, Xinsheng Yang, Qian Cheng, Murali Chintalapati, Youjiang Wu, Ken Hsieh, Kaixin Sui, Xin Meng, Yaohai Xu, Wenchi Zhang, Furao Shen, and Dongmei Zhang. 2019. Cross-dataset Time Series Anomaly Detection for Cloud Systems. In 2019 USENIX Annual Technical Conference (USENIX ATC 19). USENIX Association, Renton, WA, 1063--1076. https://www.usenix.org/conference/atc19/presentation/zhang-xu

Google Scholar

Cited By

View all

Yu GChen PHe ZYan QLuo YLi FZheng Z(2024)ChangeRCA: Finding Root Causes from Software Changes in Large Online SystemsProceedings of the ACM on Software Engineering10.1145/36437281:FSE(24-46)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643728
Wu YWang YLi JLi YChai BJiang W(2024)Understanding and Improving Change Risk Detection in Practice2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00079(717-727)Online publication date: 12-Mar-2024
https://doi.org/10.1109/SANER60148.2024.00079
Wang XYin KOuyang QWen XZhang SZhang WCao LHan JJin XPei D(2022)Identifying Erroneous Software Changes through Self-Supervised Contrastive Learning on Time Series Data2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE55969.2022.00043(366-377)Online publication date: Oct-2022
https://doi.org/10.1109/ISSRE55969.2022.00043

Recommendations

Fail through the Cracks: Cross-System Interaction Failures in Modern Cloud Systems
EuroSys '23: Proceedings of the Eighteenth European Conference on Computer Systems

Modern cloud systems are orchestrations of independent and interacting (sub-)systems, each specializing in important services (e.g., data processing, storage, resource management, etc.). Hence, cloud system reliability is affected not only by the ...
What bugs cause production cloud incidents?
HotOS '19: Proceedings of the Workshop on Hot Topics in Operating Systems

Cloud services have become the backbone of today's computing world. Runtime incidents, which adversely affect the expected service operations, are extremely costly in terms of user impacts and engineering efforts required to resolve them. Hence, such ...
Data-driven root cause analysis via causal discovery using time-to-event data
Highlights
- A novel data-driven root cause analysis framework for learning root.
- cause graphs (RCGs) from time-to-event (TTE) data.
- A framework for simulating realistic TTE data from structural causal.
- models for root cause investigations.
Abstract
Data-driven approaches to root cause analysis (RCA) have received at- tention recently due to their ability to exploit increasing data availability for more effective root cause identification in complex processes. Advancements in the field of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

HAOC '21: Proceedings of the 1st Workshop on High Availability and Observability of Cloud Systems

April 2021

29 pages

ISBN:9781450383363

DOI:10.1145/3447851

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGOPS: ACM Special Interest Group on Operating Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

EuroSys '21

EuroSys '21: Sixteenth European Conference on Computer Systems

April 26, 2021

Online, United Kingdom

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
193
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)2

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Yu GChen PHe ZYan QLuo YLi FZheng Z(2024)ChangeRCA: Finding Root Causes from Software Changes in Large Online SystemsProceedings of the ACM on Software Engineering10.1145/36437281:FSE(24-46)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643728
Wu YWang YLi JLi YChai BJiang W(2024)Understanding and Improving Change Risk Detection in Practice2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00079(717-727)Online publication date: 12-Mar-2024
https://doi.org/10.1109/SANER60148.2024.00079
Wang XYin KOuyang QWen XZhang SZhang WCao LHan JJin XPei D(2022)Identifying Erroneous Software Changes through Self-Supervised Contrastive Learning on Time Series Data2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE55969.2022.00043(366-377)Online publication date: Oct-2022
https://doi.org/10.1109/ISSRE55969.2022.00043

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

Fail through the Cracks: Cross-System Interaction Failures in Modern Cloud Systems

What bugs cause production cloud incidents?

Data-driven root cause analysis via causal discovery using time-to-event data