Graph Based Liability Analysis for the Microservice Architecture

被引：0
|
作者：
Kalinagac, Onur [1 ]
Soussi, Wissem [1 ]
Gur, Gurkan [1 ]
机构：
[1] Zurich Univ Appl Sci ZHAW InIT, CH-8401 Winterthur, Switzerland
来源：
2022 18TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM 2022): INTELLIGENT MANAGEMENT OF DISRUPTIVE NETWORK TECHNOLOGIES AND SERVICES | 2022年
关键词：
Root cause analysis; Causal Bayesian Network; microservices;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this work, we present Graph Based Liability Analysis Framework (GRALAF) for root cause analysis (RCA) of the microservices. In this Proof-of-Concept (PoC) tool, we keep track of the performance metrics of microservices, such as service response time and CPU level values, to detect anomalies. By injecting faults in the services, we construct a Causal Bayesian Network (CBN) which represents the relation between service faults and metrics. The constructed CBN is used to predict the fault probability of services under given metrics which are assigned discrete values according to their anomaly states.
引用
收藏
页码：364 / 366
页数：3
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