Microservices Monitoring with Event Logs and Black Box Execution Tracing

Cinque, Marcello; Corte, Raffaele Della; Pecchia, Antonio

doi:10.1109/tsc.2019.2940009

Cited by 36 publications

(19 citation statements)

References 18 publications

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“…Cinque et al 18 present a non-intrusive novel monitoring approach to accompany micro-services logs with black box tracing to help practitioners in making informed decisions for troubleshooting. It is thus a passive tracing solution and it aims to cope with the flexibility requirements of micro-services systems, but only on log level.…”

Section: Related Workmentioning

confidence: 99%

Towards cloud‐based unobtrusive monitoring in remote multi‐vendor environments

et al. 2021

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Nowadays, many complex multi-vendor production environments, such as telecom infrastructures in smart cities or on-board passenger information systems in trains, are based on micro-services and deployed in the cloud. From a service integrator viewpoint, building new solutions for these environments, which can host a large number of externally designed and developed micro-services, is often complex and error-prone. This is in part due to undocumented behaviour or undocumented architectural specifications of such systems. Advanced service monitoring can offer a solution to quickly detect anomalies or unexpected service interaction behaviour during on-site integration. However, the monitoring service should not have an impact on the production environment itself. Therefore, this article proposes an agent-based unobtrusive monitoring platform, capable of monitoring both internally developed and externally developed services through the use of sidecar containers. It monitors state, metrics and network traffic at micro-service level and the research was conducted as part of the DynAMo research project, a collaboration with various industry partners.Prototype evaluation proves that our solution has a negligible impact (below 0.02% CPU usage on average) on an existing micro-service environment just as other monitoring systems like Prometheus while offering additional functionality focused on multi-vendor service integration. This makes it suitable to be deployed in complex production domains to further aid on-site integration and quickly find potential new anomalies.

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Section: Related Workmentioning

confidence: 99%

Towards cloud‐based unobtrusive monitoring in remote multi‐vendor environments

et al. 2021

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“…Log-based methods. The system log is an important clue for analysis [2]. By parsing patterns and extracting features from event logs, Xu et al [15,16] built anomaly detection and identification models from historical data and used these models to analyze root causes.…”

Section: Related Workmentioning

confidence: 99%

“…where AS(Vi), Degree(Vi), and N denote the anomaly score of Vi, the degree of Vi, and the number of AAN(Vi), respectively. As for NHAN(Vi) we define: , 𝑉 𝑗 ∈ 𝑁𝐻𝐴𝑁( 𝑉 𝑖 ), (2) where x denotes the average anomaly score of HNAN(V i ). Here iScore indicates the anomalous degree of AAN(Vi), and xScore reflects the normality of NHAN(Vi).…”

Section: E Automated Anomaly Weight Updatingmentioning

confidence: 99%

“…Traditionally, system failures are usually pinpointed by checking the log and event tracking, and then the performance issues are analyzed based on monitoring tools [2]. With the increasing scale and complexity of software, service dependencies also become increasingly complex, making these tools hard to achieve the needs of troubleshooting and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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AAMR: Automated Anomalous Microservice Ranking in Cloud-Native Environment

Zhang¹

2021

International Conferences on Software Engineering and Knowledge Engineering

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Recently, it has become a trend for developers to build applications using the microservice architecture. The functionality of each application is divided into multiple independent microservices, which are interconnected to others. With the emergence of cloud-native technologies, such as Docker and Kubernetes, developers can achieve a consistent and scalable delivery for complex software applications. However, it is challenging to diagnose performance issues in microservices due to the complex runtime environments and the numerous metrics. In this paper, we propose a novel root cause analysis approach named AAMR. AAMR firstly constructs a service dependency graph based on real-time metrics. Next, it updates the anomaly weight of each microservice automatically. Finally, a PageRankbased random walk is applied for further ranking root causes, i.e., ranking potential problematic services. Experiments conducted on Kubernetes clusters show that the proposed approach achieves a good analysis result, which outperforms several state-of-the-art methods.

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“…Unfortunately, given that the scope of Reference [15] is on network security, no information about accuracy and overhead is provided. The work of Cinque et al[10] provides a tool for network traffic analysis that accompanies a system for log collection that involves application instrumentation. Its MetroFunnel component is based on pcap and can pinpoint abnormal service operation with small overhead on the performance of the applications.…”

mentioning

confidence: 99%

A Black-box Monitoring Approach to Measure Microservices Runtime Performance

Brondolin

Santambrogio

2020

ACM Trans. Archit. Code Optim.

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Microservices changed cloud computing by moving the applications’ complexity from one monolithic executable to thousands of network interactions between small components. Given the increasing deployment sizes, the architectural exploitation challenges, and the impact on data-centers’ power consumption, we need to efficiently track this complexity. Within this article, we propose a black-box monitoring approach to track microservices at scale, focusing on architectural metrics, power consumption, application performance, and network performance. The proposed approach is transparent w.r.t. the monitored applications, generates less overhead w.r.t. black-box approaches available in the state-of-the-art, and provides fine-grain accurate metrics.

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Microservices Monitoring with Event Logs and Black Box Execution Tracing

Cited by 36 publications

References 18 publications

Towards cloud‐based unobtrusive monitoring in remote multi‐vendor environments

Towards cloud‐based unobtrusive monitoring in remote multi‐vendor environments

AAMR: Automated Anomalous Microservice Ranking in Cloud-Native Environment

A Black-box Monitoring Approach to Measure Microservices Runtime Performance

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