Root Cause Analysis of Anomalies of Multitier Services in Public Clouds

Weng, Jianping; Wang, Jessie Hui; Yang, Jiahai; Yang, Yang

doi:10.1109/tnet.2018.2843805

Cited by 60 publications

(18 citation statements)

References 23 publications

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“…MicroRCA constructs an attributed graph to represent the anomaly propagation in microservices environments, which is based on the observation that anomalies propagate not only among services along the service call paths but also to services collocated on the same (virtual) machines [29], [30].…”

Section: A Attributed Graph Constructionmentioning

confidence: 99%

“…Localizing Faulty Services: We locate the faulty services from the anomalous subgraph with a graph centrality algorithm -Personalized PageRank [28], which proved a good performance in capturing anomaly propagation in previous work [10], [11], [13], [29]. In Personalized PageRank, the Personalized PageRank vector (PPV) v is regarded as the root cause score for each node.…”

Section: Faulty Services Localizationmentioning

confidence: 99%

See 1 more Smart Citation

MicroRCA: Root Cause Localization of Performance Issues in Microservices

Wu¹,

Tordsson²,

Elmroth³

et al. 2020

NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium

124

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Software architecture is undergoing a transition from monolithic architectures to microservices to achieve resilience, agility and scalability in software development. However, with microservices it is difficult to diagnose performance issues due to technology heterogeneity, large number of microservices, and frequent updates to both software features and infrastructure. This paper presents MicroRCA, a system to locate root causes of performance issues in microservices. MicroRCA infers root causes in real time by correlating application performance symptoms with corresponding system resource utilization, without any application instrumentation. The root cause localization is based on an attributed graph that model anomaly propagation across services and machines. Our experimental evaluation where common anomalies are injected to a microservice benchmark running in a Kubernetes cluster shows that MicroRCA locates root causes well, with 89% precision and 97% mean average precision, outperforming several state-of-the-art methods.

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Section: A Attributed Graph Constructionmentioning

confidence: 99%

Section: Faulty Services Localizationmentioning

confidence: 99%

MicroRCA: Root Cause Localization of Performance Issues in Microservices

Wu¹,

Tordsson²,

Elmroth³

et al. 2020

NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium

124

View full text Add to dashboard Cite

show abstract

“…These methods used forward, self, and backward random walk to heuristically locate root causes. Besides, Weng et al [21] found that anomalies occur on both the service and physical level. MicroRCA [8] correlated anomalous performance symptoms with relevant resource utilization to represent service anomalies.…”

Section: Related Workmentioning

confidence: 99%

AAMR: Automated Anomalous Microservice Ranking in Cloud-Native Environment

Zhang¹

2021

International Conferences on Software Engineering and Knowledge Engineering

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Recently, it has become a trend for developers to build applications using the microservice architecture. The functionality of each application is divided into multiple independent microservices, which are interconnected to others. With the emergence of cloud-native technologies, such as Docker and Kubernetes, developers can achieve a consistent and scalable delivery for complex software applications. However, it is challenging to diagnose performance issues in microservices due to the complex runtime environments and the numerous metrics. In this paper, we propose a novel root cause analysis approach named AAMR. AAMR firstly constructs a service dependency graph based on real-time metrics. Next, it updates the anomaly weight of each microservice automatically. Finally, a PageRankbased random walk is applied for further ranking root causes, i.e., ranking potential problematic services. Experiments conducted on Kubernetes clusters show that the proposed approach achieves a good analysis result, which outperforms several state-of-the-art methods.

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“…Many research papers on RCA focus on complex large-scale systems [1][2][3][4]; they can be grouped into the following categories:…”

Section: Related Workmentioning

confidence: 99%

A Causality Mining and Knowledge Graph Based Method of Root Cause Diagnosis for Performance Anomaly in Cloud Applications

Qiu

Yin

et al. 2020

Applied Sciences

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With the development of cloud computing technology, the microservice architecture (MSA) has become a prevailing application architecture in cloud-native applications. Many user-oriented services are supported by many microservices, and the dependencies between services are more complicated than those of a traditional monolithic architecture application. In such a situation, if an anomalous change happens in the performance metric of a microservice, it will cause other related services to be downgraded or even to fail, which would probably cause large losses to dependent businesses. Therefore, in the operation and maintenance job of cloud applications, it is critical to mine the causality of the problem and find its root cause as soon as possible. In this paper, we propose an approach for mining causality and diagnosing the root cause that uses knowledge graph technology and a causal search algorithm. We verified the proposed method on a classic cloud-native application and found that the method is effective. After applying our method on most of the services of a cloud-native application, both precision and recall were over 80%.

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Root Cause Analysis of Anomalies of Multitier Services in Public Clouds

Cited by 60 publications

References 23 publications

MicroRCA: Root Cause Localization of Performance Issues in Microservices

MicroRCA: Root Cause Localization of Performance Issues in Microservices

AAMR: Automated Anomalous Microservice Ranking in Cloud-Native Environment

A Causality Mining and Knowledge Graph Based Method of Root Cause Diagnosis for Performance Anomaly in Cloud Applications

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