AmazeMap:基于多层次影响图的微服务故障定位方法  

AmazeMap:Microservices Fault Localization Method Based on Multi-level Impact Graph

作　　者：李亚晓 李青山[1] 王璐[1] 姜宇轩 LI Ya-Xiao;LI Qing-Shan;WANG Lu;JIANG Yu-Xuan(School of Computer Science and Technology,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学计算机科学与技术学院,陕西西安710071

出　　处：《软件学报》2024年第7期3115-3140,共26页Journal of Software

基　　金：国家自然科学基金(62372351,U21B2015);陕西省科协青年人才托举计划(20220113)。

摘　　要：微服务软件系统由于其具有大量复杂的服务依赖关系和组件化模块,一个服务发生故障往往造成与之相关的1个或多个服务发生故障,导致故障定位的难度不断提高.因此,如何有效地检测系统故障、快速而准确地定位故障根因问题,是当前微服务领域研究的重点.现有研究一般通过分析故障对服务、指标的作用关系来构建故障关系模型,但存在运维数据利用不充分、故障信息建模不全面、根因定位粒度粗等问题,因此提出了AmazeMap方法.该方法设计了多层次故障影响图建模方法以及基于多层次故障影响图的微服务故障定位方法.其中:多层次故障影响图建模方法通过挖掘系统运行时指标时序数据与链路数据,考虑不同层次间的相互关系,能够较全面地建模故障信息;基于多层次故障影响图的微服务故障定位方法通过缩小故障影响范围,从服务实例和指标两个方面发现根因,输出最有可能的故障根因节点和指标序列.基于开源基准微服务系统和AIOps挑战赛数据集,从有效性和效率两个方面设计了微服务软件故障定位实验,并与现有方法进行对比,实验结果验证了AmazeMap的有效性、准确性和效率.Due to the large number of complex service dependencies and componentized modules,a failure in one service often causes one or more related services to fail,making it increasingly difficult to locate the cause of the failure.Therefore,how to effectively detect system faults and locate the root cause of faults quickly and accurately is the focus of current research in the field of microservices.Existing research generally builds a failure relationship model by analyzing the relationship between failures and services and metrics,but there are problems such as insufficient utilization of operation and maintenance data,incomplete modeling of fault information,coarse granularity of root cause localization,etc.Therefore,this study proposes AmazeMap,for which a multi-level fault impact graph modeling method and a microservice fault localization method are designed based on the fault impact graph.Specifically,the multi-level fault impact graph modeling method can comprehensively model the fault information by mining the collected temporal metric data and trace data while system running and considering the interrelationships between different levels;the fault localization method narrows the scope of fault impact,discovers the root cause from service instances and metrics,and finally outputs the most probable root cause of fault and metrics sequence.Based on an open-source benchmark microservice system and the AIOps contest dataset,this study designs experiments to validate AmazeMap,and also compares it with the existing methods.The results confirm AmazeMap’s effectiveness,accuracy,and efficiency.

关 键 词：微服务 故障定位 多层次故障影响图 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]