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

Jin, Mingxu; Lv, Aoran; Zhu, Yuanpeng; Wen, Zijiang; Zhong, Yu-Bin; Zhao, Zexin; Wu, Jing; Li, Hejie; He, Hanheng; Chen, Fengyi

doi:10.1109/access.2020.3044610

Cited by 24 publications

(27 citation statements)

References 37 publications

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“…Using dynamic techniques, several different approaches become feasible that cannot be done via static techniques, for example, performance analysis and optimization techniques as well as other metrics-based analyses [12,32,36,39,40]. A specific subset of dynamic techniques is commonly applied to fault analysis and root cause analysis: log analysis and execution trace analysis are perfect for this task, as they examine traces directly related to program execution [35,40,[42][43][44].…”

Section: Rq1: Methods and Techniques Usedmentioning

confidence: 99%

“…An article by Samardzic et al [40] uses log analysis as well, basing its results on runtime logs collected from six real-world microservices commonly used in the retail domain to determine how microservice runtime performance degrades. An algorithm proposed by Jin et al [43] relies on execution traces. It first screens a potential anomalous time period during a system's runtime.…”

Section: Dynamic Analysismentioning

confidence: 99%

“…A new experimental framework presented by Wu et al [55] aims to better localize root causes of failures in microservice systems and detects performance issues by correlating their symptoms with system resource use. 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.…”

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%

“…We also found little relevant research on using machine learning as a primary method for microservice analysis. The only notable work that used machine learning as a primary method of analysis was written by Jin et al [43], where a Robust Component Principal Analysis algorithm was used alongside dynamic analysis to detect anomalies. Zhou et al [45] briefly mention the potential of using machine learning-based algorithms for improving fault localization.…”

Section: Rq4: Future Research Directionsmentioning

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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