LogDC: Problem Diagnosis for Declartively-Deployed Cloud Applications with Log

Xu, Jie; Chen, Pengfei; Yang, Lin; Meng, Fan Jing; Wang, Ping

doi:10.1109/icebe.2017.52

Cited by 27 publications

(7 citation statements)

References 10 publications

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“…In recent years, many solutions have been proposed to identify root causes in distributed systems, clouds and microservices. Log-based approaches [14]- [17] build problem detection and identification models based on logs parsing. Even though log-based approaches can discovery more informational causes, they are hard to work in real time and require abnormal information to be hidden in logs.…”

Section: Related Workmentioning

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

126

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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: Related Workmentioning

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

126

View full text Add to dashboard Cite

show abstract

“…al [22] proposed an unstructured log analysis technique of anomalies detection for distributed systems. Xu et al [23] introduced LogDC, a log model based problem diagnosis tool for cloud applications with the full-lifecycle Kubernetes logs. Jia et al [24] proposed an approach for automatic anomaly detection based on logs.…”

Section: E Threats To Validitymentioning

confidence: 99%

MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems

Liu

Peng

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

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Availability issues of industrial microservice systems (e.g., drop of successfully placed orders and processed transactions) directly affect the running of the business. These issues are usually caused by various types of service anomalies which propagate along service dependencies. Accurate and high-efficient root cause localization is thus a critical challenge for large-scale industrial microservice systems. Existing approaches use service dependency graph based analysis techniques to automatically locate root causes. However, these approaches are limited due to their inaccurate detection of service anomalies and inefficient traversing of service dependency graph. In this paper, we propose a high-efficient root cause localization approach for availability issues of microservice systems, called MicroHECL. Based on a dynamically constructed service call graph, MicroHECL analyzes possible anomaly propagation chains, and ranks candidate root causes based on correlation analysis. We combine machine learning and statistical methods and design customized models for the detection of different types of service anomalies (i.e., performance, reliability, traffic). To improve the efficiency, we adopt a pruning strategy to eliminate irrelevant service calls in anomaly propagation chain analysis. Experimental studies show that MicroHECL significantly outperforms two state-of-the-art baseline approaches in terms of both accuracy and efficiency. MicroHECL has been used in Alibaba and achieves a top-3 hit ratio of 68% with root cause localization time reduced from 30 minutes to 5 minutes.

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“…The authors in [7,8] introduced a classic log mining method to diagnose and locate anomalies in traditional distributed systems. According to the research in [9][10][11], the log mining method also plays an important role in the RCA of cloud applications. However, not all abnormal behaviors are recorded in logs: in many cases of RCA, in addition to log mining, O&M personnel have to combine their domain experience to find the root cause.…”

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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LogDC: Problem Diagnosis for Declartively-Deployed Cloud Applications with Log

Cited by 27 publications

References 10 publications

MicroRCA: Root Cause Localization of Performance Issues in Microservices

MicroRCA: Root Cause Localization of Performance Issues in Microservices

MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems

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

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