Name
Title | ||
|---|---|---|
Learning-Based Anomaly Cause Tracing With Synthetic Analysis Of Logs From Multiple Cloud Service Components |
Abstract | ||
|---|---|---|
It is critical for a reliable cloud to effectively find out root causes of the cloud service anomalies for efficacious treatment. System logs are widely used for anomaly detection and analysis. Many efforts have been made to handle massive cloud logs automatically. However, existing work can still not effectually make comprehensive use of logs from multiple cloud service components to locate the causes of cloud service anomalies automatically. In this paper, we propose a learning-based approach for fine-grained deep cloud service anomaly cause tracing by synthetically utilizing logs from multiple service components of a cloud. We focus on uncovering root causes of anomalies corresponding to system executions of each user operation rather than roughly taking various system tasks as a whole. Log patterns are learned from past experience of system runs with anomalies occurred before, where the mined log event sequences to represent system behaviors related to each user operation are treated as natural language sequences. When an anomaly is to be diagnosed, the corresponding log patterns can be recognized for root cause identification. We implemented and evaluated our approach in OpenStack. Experimental results show that our approach can effectively trace the root causes for anomalies in cloud environments. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/COMPSAC.2019.00019 | 2019 IEEE 43RD ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), VOL 1 |
DocType | ISSN | Citations |
Conference | 0730-3157 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |