FacGraph: Frequent Anomaly Correlation Graph Mining for Root Cause Diagnose in Micro-Service Architecture
| Autor: | Ping Wang, Meng Ma, Weilan Lin, Disheng Pan |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
Computer science computer.internet_protocol business.industry Breadth-first search Cloud computing 02 engineering and technology Service-oriented architecture Root cause computer.software_genre 01 natural sciences Server 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Leverage (statistics) 020201 artificial intelligence & image processing Data mining Architecture business computer |
| Zdroj: | IPCCC |
| Popis: | Micro-service architecture is a promising paradigm to develop, deploy and maintain applications using independent and autonomous cloud services. Nowadays, increasingly applications are embracing this model. However, it is difficult and time-consuming to diagnose and identify the actual root cause when anomalies occurs in micro-service architecture due to various factors. This paper introduces a novel framework for anomaly investigation and root cause identification in micro-service architecture. The novelty in our work lies on: (1) Different from existing solutions, in our framework, we propose a frequent pattern mining algorithm on anomaly correlation graph, named FacGraph, to discover root cause services. (2) We leverage breadth first ordered string (BFOS) to reduce the time-consumption of the frequent graph mining (FSM) (3) We further develop a distributed version of FacGraph to improve its paralleled computing efficiency. We evaluate our framework in real production environment IBM Bluemix. Result demonstrate that FacGraph outperforms other methods in diagnosis accuracy and offers a fast identification of root cause service when an anomaly occurs. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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