Name
Title | ||
|---|---|---|
MS-Rank: Multi-Metric and Self-Adaptive Root Cause Diagnosis for Microservice Applications |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICWS.2019.00022 | 2019 IEEE International Conference on Web Services (ICWS) |
Keywords | Field | DocType |
microservice architecture,root cause,anomaly diagnosis,impact graph,cloud computing | Graph,Data mining,Architecture,Heuristic,IBM,Random walk,Computer science,Self adaptive,Root cause,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-7281-2718-7 | 1 | 0.35 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Ma | 1 | 82 | 12.29 |
| Weilan Lin | 2 | 4 | 2.11 |
| Disheng Pan | 3 | 3 | 1.74 |
| Ping Wang | 4 | 149 | 14.37 |