Weighted Sampling of Execution Traces

Las-Casas, Pedro; Mace, Jonathan; Guedes, Dorgival; Fonseca, Rodrigo

doi:10.1145/3267809.3267841

Cited by 25 publications

(10 citation statements)

References 9 publications

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“…To collect runtime data from a system, various adaptive sampling techniques (Bartocci et al, 2012;Ding et al, 2015) have been proposed. For example, an approach for weighted trace sampling relies on the clustering of execution graphs, with the goal to maximize the diversity of collected execution traces (Las-Casas et al, 2018). (Narayanappa et al, 2010) proposed ''Property-Aware Program Sampling'', a profiling technique that uses program slicing in the context of statistical sampling-based instrumentation.…”

Section: Related Workmentioning

confidence: 99%

AMon: A domain-specific language and framework for adaptive monitoring of Cyber–Physical Systems

Vierhauser

Wohlrab

Stadler

et al. 2023

Journal of Systems and Software

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Section: Related Workmentioning

confidence: 99%

AMon: A domain-specific language and framework for adaptive monitoring of Cyber–Physical Systems

Vierhauser

Wohlrab

Stadler

et al. 2023

Journal of Systems and Software

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“…Las‐Casas et al 44 referred to a solution for the method of sampling a variety of status codes with a uniform distribution. Based on the observation that more than 85% of recorded events are in good condition, they introduced a model for identifying the uniform distribution of events with hierarchical clustering algorithms.…”

Section: Related Workmentioning

confidence: 99%

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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“…Property graphs provide a powerful machinery to represent these traces; indeed, as a graph, relationships are as important as the data represented in nodes. There is existing work considering every trace as a Directed Acyclic Graph (DAG) of spans linked by causal relationship [16], [17]. In our previous work, we leveraged OpenTelemetry semantic to exhibit the layered structure of the network and add a hierarchical structure to these span graphs.…”

Section: A Getting Individual Trace Graphsmentioning

confidence: 99%

A Tracing Based Model to Identify Bottlenecks in Physically Distributed Applications

Casse

Berthou

Owezarski

et al. 2022

2022 International Conference on Information Networking (ICOIN)

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Weighted Sampling of Execution Traces

Cited by 25 publications

References 9 publications

AMon: A domain-specific language and framework for adaptive monitoring of Cyber–Physical Systems

AMon: A domain-specific language and framework for adaptive monitoring of Cyber–Physical Systems

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

A Tracing Based Model to Identify Bottlenecks in Physically Distributed Applications

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