Root cause detection in a service-oriented architecture

Kim, Myung–Hwan; Sumbaly, Roshan; Shah, Sam

doi:10.1145/2465529.2465753

Cited by 42 publications

(38 citation statements)

References 39 publications

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“…When a certain external factor causes an anomaly, the observed node affected by the external factor shows a highly correlated metric pattern in the anomaly time window. A pseudo-anomaly clustering algorithm [1] could be employed to solve such problems, and we will consider this part in future work.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…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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“…In addition, there are many metrics-based approaches [5], [9]- [13], [24], [25], as well as this work. These use metrics from applications and/or additional infrastructure levels to construct a causality graph that is used to infer root causes.…”

Section: Related Workmentioning

confidence: 99%

“…MonitorRank [13], Microscope [12] and CloudRanger [11] identify root causes based on application level metrics only. MonitorRank considers internal and external factors, and proposes a pseudo-anomaly clustering algorithm to classify external factors, then traverses the provided service call graph with a random walk algorithm to identify anomalous services.…”

Section: Related Workmentioning

confidence: 99%

“…These either require the application to be instrumented (e.g., [8], [9]) or numerous metrics to be analyzed (e.g., [5], [10]). A third class of approaches [11]- [13] avoid these limitations by building a causality graph and inferring the causes along the graph based on application-level metrics. With this approach, potential root causes are commonly ranked through the correlation between back-end services and frontend services.…”

Section: Introductionmentioning

confidence: 99%

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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

104

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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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TraceRank: Abnormal service localization with dis‐aggregated end‐to‐end tracing data in cloud native systems

Huang

Chen

2021

J Software Evolu Process

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Modern cloud native applications are generally built with a microservice architecture. To tackle various performance problems among a large number of services and machines, an end‐to‐end tracing tool is always equipped in these systems to track the execution path of every single request. However, it is nontrivial to conduct root cause analysis of anomalies with such a large volume of tracing data. This paper proposes a novel system named TraceRank to identify and locate abnormal services causing performance problems with dis‐aggregated end‐to‐end traces. TraceRank mainly includes an anomaly detection module and a root cause analysis module. The root cause analysis procedure is triggered when an anomaly is detected. To fully leverage the information provided by the tracing data, both the spectrum analysis and the PageRank‐based random walk methods are introduced to pinpoint abnormal services. The experiments in TrainTicket and Bookinfo microservice benchmarks and a real‐world system show that TraceRank can locate root causes with 90% in Precision and 86% in Recall. TraceRank has up to 10% improvement compared with several state‐of‐the‐art approaches in both Precision and Recall. Finally, TraceRank has good scalability and a low overhead to adapt to large‐scale microservice systems.

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Root cause detection in a service-oriented architecture

Cited by 42 publications

References 39 publications

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

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

MicroRCA: Root Cause Localization of Performance Issues in Microservices

TraceRank: Abnormal service localization with dis‐aggregated end‐to‐end tracing data in cloud native systems

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