CloudRanger: Root Cause Identification for Cloud Native Systems

Wang, Ping; Xu, Jie; Ma, Meng; Lin, Weilan; Pan, Disheng; Wang, Yuan; Chen, Pengfei

doi:10.1109/ccgrid.2018.00076

Cited by 104 publications

(78 citation statements)

References 14 publications

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“…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%

“…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%

“…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%

See 2 more Smart Citations

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Faulty Services Localizationmentioning

confidence: 99%

Section: Introductionmentioning

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

126

View full text Add to dashboard Cite

show abstract

“…Xu et al presented a novel approach to API performance monitoring, which recognizes performance problems by response time deviation from a baseline response time/throughput model that are created and continuously updated through online learning [12]. Wang et al proposed a dynamic causal relationship analysis approach to construct impact graphs amongst applications without given the topology [14]. Yan et al described the design and development of a Generic Root Cause Analysis platform (G-RCA) for service quality management (SQM) in large IP networks [19].…”

Section: Introductionmentioning

confidence: 99%

An Anomaly Detection Algorithm for Microservice Architecture Based on Robust Principal Component Analysis

Jin

Zhu

et al. 2020

IEEE Access

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“…Extensive studies have been done to resolve performance problems in distributed systems such as anomaly detection [6]- [8], root cause analysis [2], [9]- [11]. In these studies, the researchers need to deploy their own microservice benchmarks and inject some faults into these systems in order to get abnormal cases.…”

Section: Introductionmentioning

confidence: 99%

A Framework of Virtual War Room and Matrix Sketch-Based Streaming Anomaly Detection for Microservice Systems

Chen

2020

IEEE Access

Self Cite

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Recently, microservice has been a popular architecture to construct cloud-native systems. This novel architecture brings agility and accelerates the software development process significantly. However, it is not easy to manage and operate microservice systems due to their scale and complexity. Many approaches are proposed to automatically operate microservice systems such as anomaly detection. Nevertheless, those methods cannot be sufficiently validated and compared due to a lack of real microservice systems, which leads to the slow process of intelligent operation. These challenges inspire us to build a system named ''VWR'', a framework of Virtual War Room for operating microservice applications which allows users to simulate their microservice architectures with low overhead and inject multiple types of faults into the microservice system with chaos engineering. VWR can mimic user requests and record the end-to-end tracing data (i.e., service call chains) for each request in a way consistent with OpenTracing. With easily designed tests and the produced streaming tracing data, the users can validate the performance of their intelligent operation algorithms and improve the algorithms as needed. Besides, based on the streaming tracing data generated by VWR, we introduce a novel unsupervised anomaly detection algorithm based on Matrix Sketch and set it as a default intelligent operation algorithm in VWR. This algorithm can detect anomalies by analyzing high-dimensional performance data collected from a microservice system in a streaming manner. The experimental result in VWR shows that the matrix sketch based method can precisely detect anomalies in microservice systems and outperform some widely used anomaly detection methods such as isolation forest in some scenario. We believe more approaches on the intelligent operation of microservice systems can be constructed based on VWR. INDEX TERMS Microservice, virtual war room, matrix sketch, anomaly detection, chaos engineering.

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CloudRanger: Root Cause Identification for Cloud Native Systems

Cited by 104 publications

References 14 publications

MicroRCA: Root Cause Localization of Performance Issues in Microservices

MicroRCA: Root Cause Localization of Performance Issues in Microservices

An Anomaly Detection Algorithm for Microservice Architecture Based on Robust Principal Component Analysis

A Framework of Virtual War Room and Matrix Sketch-Based Streaming Anomaly Detection for Microservice Systems

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