Detecting Anomalous Behavior of Black-Box Services Modeled with Distance-Based Online Clustering

Gulenko, Anton; Schmidt, Florian; Acker, Alexander; Wallschläger, Marcel; Kao, Odej; Liu, Feng

doi:10.1109/cloud.2018.00134

Cited by 33 publications

(24 citation statements)

References 3 publications

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“…Anomaly detection is the starting point of root cause localization. MicroRCA leverages the unsupervised learning algorithm Distance-Based online clustering BIRCH [27] anomaly detection method. We use the slow response time of a microservice as the definition of an anomaly.…”

Section: Anomaly Detectionmentioning

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

Self Cite

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.

show abstract

Section: Anomaly Detectionmentioning

confidence: 99%

“…(3) Memory leak: we use stress-ng to allocate memory continuously. As microservice carts and orders are CPU and memory intensive services, and memory leak causes CPU overhead [27], we only provision 1 virtual machine. The details of the injected faults are described in Table III.…”

Section: A Experimental Setupmentioning

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

Self Cite

124

View full text Add to dashboard Cite

show abstract

“…In the system, we detect the performance anomaly on the response times between two interactive services (collected by service mesh) using a unsupervised learning method: BIRCH clustering [6]. When a response time deviates from their normal status, it is detected as an anomaly and trigger the subsequent performance diagnosis procedures.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Performance Diagnosis in Cloud Microservices Using Deep Learning

Bogatinovski

Nedelkoski

et al. 2021

Lecture Notes in Computer Science

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Microservice architectures are increasingly adopted to design large-scale applications. However, the highly distributed nature and complex dependencies of microservices complicate automatic performance diagnosis and make it challenging to guarantee service level agreements (SLAs). In particular, identifying the culprits of a microservice performance issue is extremely difficult as the set of potential root causes is large and issues can manifest themselves in complex ways. This paper presents an application-agnostic system to locate the culprits for microservice performance degradation with fine granularity, including not only the anomalous service from which the performance issue originates but also the culprit metrics that correlate to the service abnormality. Our method first finds potential culprit services by constructing a service dependency graph and next applies an autoencoder to identify abnormal service metrics based on a ranked list of reconstruction errors. Our experimental evaluation based on injection of performance anomalies to a microservice benchmark deployed in the cloud shows that our system achieves a good diagnosis result, with 92% precision in locating culprit service and 85.5% precision in locating culprit metrics.

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“…For each response time which is a time series, MicroDiag applies the Birch clustering and detects it as an anomaly if multiple clusters are detected with a given threshold. Birch clustering is an efficient algorithm for anomaly detection which detects anomalies in real-time without relying on any historical failure data [18].…”

Section: Anomaly Detectionmentioning

confidence: 99%

MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems

Tordsson

Bogatinovski

et al. 2021

2021 IEEE/ACM International Workshop on Cloud Intelligence (CloudIntelligence)

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Microservice architecture has emerged as a popular pattern for developing large-scale applications for its benefits of flexibility, scalability, and agility. However, the large number of services and complex dependencies make it difficult and timeconsuming to diagnose performance issues. We propose Micro-Diag, an automated system to localize root causes of performance issues in microservice systems at a fine granularity, including not only locating the faulty component but also discovering detailed information for its abnormality. MicroDiag constructs a component dependency graph and performs causal inference on diverse anomaly symptoms to derive a metrics causality graph, which is used to infer root causes. Our experimental evaluation on a microservice benchmark running in a Kubernetes cluster shows that MicroDiag localizes root causes well, with 97% precision of the top 3 most likely root causes, outperforming state-of-the-art methods by at least 31.1%.

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Detecting Anomalous Behavior of Black-Box Services Modeled with Distance-Based Online Clustering

Cited by 33 publications

References 3 publications

MicroRCA: Root Cause Localization of Performance Issues in Microservices

MicroRCA: Root Cause Localization of Performance Issues in Microservices

Performance Diagnosis in Cloud Microservices Using Deep Learning

MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems

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