Inferring Calling Relationship Based on External Observation for Microservice Architecture

Kitajima, Shinya; Matsuoka, Naoki

doi:10.1007/978-3-319-69035-3_16

Cited by 5 publications

(9 citation statements)

References 4 publications

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“…[9][10][11][12] Due to the multiplicity of microservices in distributed systems in large software, tracing distributed anomalies is not a straightforward task. 6,13,14 It is often arduous to pinpoint the exact fault and address the specific microservice affected. Several tools have been proposed in this regard that provides different types of statistics.…”

Section: Suspendingdockermentioning

confidence: 99%

“…In addition to monolithic architecture, software based on microservice architecture also uses log analysis to detect anomalies. In Reference 6, Shinya Kitajima and Matsuaka provided a solution to identify deviations from service relationships based on the normal relationship between services and service execution time. Sambasivan et al 34 attempted to plot the relationship between services by considering only the Gateway service.…”

Section: Related Workmentioning

confidence: 99%

“…There is a growing adoption of the microservice architecture in the software industry 1‐4 . Microservices are small, independent applications with separate functions and responsibilities 5‐7 . With the increasing complexity of implementing microservice systems in the cloud, 8 troubleshooting bottlenecks and faults is a notoriously complicated and time‐consuming task 9‐12 .…”

Section: Introductionmentioning

confidence: 99%

“…With the increasing complexity of implementing microservice systems in the cloud, 8 troubleshooting bottlenecks and faults is a notoriously complicated and time‐consuming task 9‐12 . Due to the multiplicity of microservices in distributed systems in large software, tracing distributed anomalies is not a straightforward task 6,13,14 . It is often arduous to pinpoint the exact fault and address the specific microservice affected.…”

Section: Introductionmentioning

confidence: 99%

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Detection of microservice‐based software anomalies based on OpenTracing in cloud

et al. 2023

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Summary Today, the noticeable tendency of the software industry to break large software projects into loosely coupled modules through a microservice‐based architecture is more than ever. This is because of advantages such as scalability, independence, smaller and faster deployments, improved fault isolation, and flexibility. On the other hand, it should be noted that with the growth of microservice architecture, new complexities have emerged. We need to have a mature DevOps team to handle the complexity involved in maintaining and supporting systems, namely functional and non‐functional monitoring (anomaly monitoring and detection). This challenge can lead to a lot of software development time being spent monitoring and identifying anomalies. Existing approaches are not accurate enough to identify anomalies, and if they are able to identify them, they are unable to identify the category of the anomaly. Our approach in this research is to use distributed tracing with the help of machine learning algorithms to identify performance anomalies, the exact location of each anomaly, and predict its category. In this research, we implemented a software based on microservice architecture and then created a variety of anomalies over time (e.g., physical resources, virtual resources, database, application) to be able to evaluate the proposed model. The resulting dataset is publicly available. Our simulation results show that the proposed model is able to accurately identify the anomalies with 98% accuracy and their category with 99% accuracy.

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Section: Suspendingdockermentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of microservice‐based software anomalies based on OpenTracing in cloud

et al. 2023

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“…Kitajima et al [22] propose a method to extract information from calls between services to infer their relationship. The authors use a dynamic approach to gathering information from the running system.…”

Section: Related Workmentioning

confidence: 99%

A method for monitoring the coupling evolution of microservice-based architectures

Apolinário

França

2021

J Braz Comput Soc

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The microservice architecture is claimed to satisfy ongoing software development demands, such as resilience, flexibility, and velocity. However, developing applications based on microservices also brings some drawbacks, such as the increased software operational complexity. Recent studies have also pointed out the lack of methods to prevent problems related to the maintainability of these solutions. Disregarding established design principles during the software evolution may lead to the so-called architectural erosion, which can end up in a condition of unfeasible maintenance. As microservices can be considered a new architecture style, there are few initiatives to monitoring the evolution of software microservice-based architectures. In this paper, we introduce the SYMBIOTE method for monitoring the coupling evolution of microservice-based systems. More specifically, this method collects coupling metrics during runtime (staging or production environments) and monitors them throughout software evolution. The longitudinal analysis of the collected measures allows detecting an upward trend in coupling metrics that could represent signs of architectural degradation. To develop the proposed method, we performed an experimental analysis of the coupling metrics behavior using artificially generated data. The results of these experiment revealed the metrics behavior in different scenarios, providing insights to develop the analysis method for the identification of architectural degradation. We evaluated the SYMBIOTE method in a real-case open source project called Spinnaker. The results obtained in this evaluation show the relationship between architectural changes and upward trends in coupling metrics for most of the analyzed release intervals. Therefore, the first version of SYMBIOTE has shown potential to detect signs of architectural degradation during the evolution of microservice-based architectures.

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Research on Service Dependency Mining Technology Based on Tag Extension and Bipartite Graph

Zhang

Cao

2019

J. Phys.: Conf. Ser.

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Due to the rapid development of service portfolio model on network, service computing has become a hot topic in recent years. It is important for both service management and service recommendation to clarify the dependencies between services. Tag, as a widely used network information resource in the current service field, can intuitively reflect the function, purpose and other attributes of the service, and it has become a common resource in the field of service discovery and combination. Tag sparsity, however, will reduce the accuracy of service relationship analysis. Therefore, this paper proposes a service dependency mining method based on tag extension and bipartite graph. By extending the existing tags and abstracting the combination of service and the relationship between the extension tags into a bipartite graph, dependency propagation mechanism in bipartite graph is applied to improve the comprehensiveness and accuracy of the service dependency mining. The programmable Web APIs and Mashups are used to show the validity of the service dependency mining method.

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Inferring Calling Relationship Based on External Observation for Microservice Architecture

Cited by 5 publications

References 4 publications

Detection of microservice‐based software anomalies based on OpenTracing in cloud

Detection of microservice‐based software anomalies based on OpenTracing in cloud

A method for monitoring the coupling evolution of microservice-based architectures

Research on Service Dependency Mining Technology Based on Tag Extension and Bipartite Graph

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