MS-Rank: Multi-Metric and Self-Adaptive Root Cause Diagnosis for Microservice Applications
| Autor: | Weilan Lin, Disheng Pan, Meng Ma, Ping Wang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Root cause diagnosis
Computer science business.industry 020206 networking & telecommunications 020207 software engineering Self adaptive Cloud computing 02 engineering and technology Root cause computer.software_genre Random walk 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Data mining IBM Web service business computer |
| Zdroj: | ICWS |
| Popis: | This paper presents a self-adaptive root cause diagnosis framework, named MS-Rank, to analyze multiple metrics collected from micro-service architecture. MS-Rank decomposes the task into four phases: impact graph construction, random walk diagnosis, result precision calculation and metrics weight update. First, we introduce a series of basic and implied metrics into MS-Rank, and design an impact graph construction algorithm to discover causal relationship between services during anomalies. Second, we propose a random walk algorithm with forward, selfward and backward transitions to heuristically identify the root cause service. Third, we establish a self-optimizing mechanism to dynamically update the confidence weight of different metrics according to their diagnosis precision. We develop a prototype system and integrate MS-Rank into IBM Cloud, to validate and compare it with selected benchmarks. Experimental results show that MS-Rank offers fast identification and precise diagnosis result. In multiple rounds of diagnosis, MS-Rank optimizes itself effectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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