Graph-based trace analysis for microservice architecture understanding and problem diagnosis

Guo, Xiaofeng; Peng, Xin; Wang, Hanzhang; Li, Wanxue; Jiang, Huai; Ding, Dan; Xie, Tao; Su, Liangfei

doi:10.1145/3368089.3417066

Cited by 69 publications

(39 citation statements)

References 12 publications

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“…Wang et al [8 ] demonstrated Grano, an end-to-end anomaly detection and root cause analysis system for cloud native distributed data platform by providing a holistic view of the system component topology, alarms and application events. Guo et al [3] proposed a graph-based trace analysis approach for understanding architecture and diagnosing problems of microservice systems. There approaches the developers to manually detect the root causes with the aid of trace and log visualization.…”

Section: E Threats To Validitymentioning

confidence: 99%

“…Microservice architecture has been the latest trend in building cloud-native applications and more and more companies have chosen to migrate from the so-called monolithic architecture to microservice architecture [1]- [3]. Industrial microservice systems can include hundreds to thousands of services.…”

Section: Introductionmentioning

confidence: 99%

“…Existing approaches cannot efficiently support the root cause localization of availability issues for large-scale microservice systems. Developers in industry often rely on visualization tools to analyze logs and traces to identify possible anomalies and anomaly propagation chains to locate root causes [2 ], [3]. Researchers have proposed approaches for automatic root cause localization for microservice or the broader servicebased systems using trace analysis [4], [5] or service dependency graph [6]- [12].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: E Threats To Validitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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)

Self Cite

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“…Their proposed algorithm for Root Cause Analysis then traverses over the created graph. An article by [46] first describes gathering span logs from an execution trace of a system, then using this data to assemble a graph with traces, paths, and business flows. This graph is meant to make it easier for developers to understand the architecture of their microservice systems and diagnose future problems.…”

Section: Graph-based Analysismentioning

confidence: 99%

“…The scheme proposed generates service dependency graphs that enable developers to analyze dependency relationships between microservices and between services and scenarios. Guo et al [46] use dynamic analysis in the form of a graph-based method to trace through the executions of requests and better diagnose various problems in microservice architectures. Various architectural tactics identified by Marquez et al [30] solve security-related faults or prevent them from occurring in the first place.…”

Section: Fault Analysismentioning

confidence: 99%

On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study

et al. 2021

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Microservice architecture has become the leading design for cloud-native systems. The highly decentralized approach to software development consists of relatively independent services, which provides benefits such as faster deployment cycles, better scalability, and good separation of concerns among services. With this new architecture, one can naturally expect a broad range of advancements and simplifications over legacy systems. However, microservice system design remains challenging, as it is still difficult for engineers to understand the system module boundaries. Thus, understanding and explaining the microservice systems might not be as easy as initially thought. This study aims to classify recently published approaches and techniques to analyze microservice systems. It also looks at the evolutionary perspective of such systems and their analysis. Furthermore, the identified approaches target various challenges and goals, which this study analyzed. Thus, it provides the reader with a roadmap to the discipline, tools, techniques, and open challenges for future work. It provides a guide towards choices when aiming for analyzing cloud-native systems. The results indicate five analytical approaches commonly used in the literature, possibly in combination, towards problems classified into seven categories.

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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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Graph-based trace analysis for microservice architecture understanding and problem diagnosis

Cited by 69 publications

References 12 publications

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

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

On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study

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

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