Localizing Failure Root Causes in a Microservice through Causality Inference

Meng, Yuan; Zhang, Shenglin; Sun, Yongqian; Zhang, Ruru; Hu, Zhirun; Zhang, Yiyin; Jia, Chen-Yang; Wang, Zhaogang; Pei, Dan

doi:10.1109/iwqos49365.2020.9213058

Cited by 84 publications

(61 citation statements)

References 17 publications

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“…Like model-based techniques, graph-based analysis depends on representing the microservice system as a graph and then analyzing that structure, exploiting microservices' natural graph-like connections. Graph-based techniques are commonly used for detecting faults or performing root cause analysis [47,52,54,55], as well as performing monolithto-microservice migration by representing an existing monolith as a graph that can be segmented into microservices [23,24,48,49]. However, it can also be used in tracing patterns [27,28].…”

Section: Rq1: Methods and Techniques Usedmentioning

confidence: 99%

“…A representation of microservices based on a graph has an advantage in easy visualization and being able to naturally represent the graph-like connections between the services. Graph-based analysis was used most commonly in papers to handle root cause analysis, with papers by Brandon et al [47], Wu et al and Meng et al [54,55] creating graphs based on a microservice system in an erroneous state, using nodes representing the clients, hosts, databases, etc. and then tracing back through the created graph to determine an error's most likely root causes.…”

Section: Graph-based Analysismentioning

confidence: 99%

“…Jin et al [43] propose an algorithm to detect time-consuming anomalies in a microservice architecture that is based on dynamic analysis of execution traces with a root cause approach in mind. Meng et al [54] establish a new framework to find failure cases in microservice systems tested with real-world failure tickets. Guan et al [52] design and implement a prototype to perform graph-based analysis on a microservice system in an anomalous state and determine the top candidates for the root cause of the anomaly.…”

Section: Fault Analysismentioning

confidence: 99%

“…The main goal we found was that of fault analysis, either by detecting and preventing faults [2,30,41,42,[44][45][46]53], or determining their origin through root cause analysis [43,47,52,54,55]. It is apparent that identification of the cause is important, especially given that microservices run large enterprise systems.…”

Section: Rq2: Goals Addressed By the Papersmentioning

confidence: 99%

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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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Section: Rq1: Methods and Techniques Usedmentioning

confidence: 99%

Section: Graph-based Analysismentioning

confidence: 99%

Section: Fault Analysismentioning

confidence: 99%

Section: Rq2: Goals Addressed By the Papersmentioning

confidence: 99%

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On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study

et al. 2021

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“…However, it requires instrumentation to the source code; Meanwhile, its performance may decrease when re-training is frequently required to follow up the updates in microservices. Loud [10] and MicroCause [11] identify the culprit metrics by constructing the causality graph of the key performance metrics. However, they require anomaly detection to be performed on all gathered metrics, which might introduce many false positives and decrease the accuracy of causes localization.…”

Section: Related Workmentioning

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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The operation and maintenance governance of microservices architecture systems: A systematic literature review

Wang

Jiang

Wang

et al. 2022

J Software Evolu Process

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Due to its development agility, continuous delivery, scalability and other characteristics, the microservice architecture systems (MASs) have provided complex business functions to hundreds of millions of users in many application fields. The operation and maintenance governance for a large number of microservices with complex relationships is crucial to ensuring the stability and reliability of an MAS. Although this research field has received certain attention and produced some innovative results, there is a lack of systematic reviews covering the different aspects of it. In this context, the central objective of this study is to carry out a systematic literature review (SLR) in this field, in an attempt to review existing issues, discuss the main trends, and share the findings with the academia. As a result, we start from more than 500 scientific papers published from 2009 to 2021 and extract 144 most significant papers, identify that the main research directions of this field include load balancing, fault detection, and autoscaling. Subsequently, we provide a comprehensive description of these research directions, discuss them in particular detail. We also determine limitations of current work and discuss new directions worth exploring in the future. Consequently, the outcomes will assist professionals and experts in the industry as well as academic researchers to focus more on operation and maintenance governance of MASs and further improve the relevant methods and theoretical systems in this field.

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Localizing Failure Root Causes in a Microservice through Causality Inference

Cited by 84 publications

References 17 publications

On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study

On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study

Performance Diagnosis in Cloud Microservices Using Deep Learning

The operation and maintenance governance of microservices architecture systems: A systematic literature review

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