Anomaly detection and failure root cause analysis in (micro) service-based cloud applications: A survey
The proliferation of services and service interactions within microservices and cloud-native
applications, makes it harder to detect failures and to identify their possible root causes …
applications, makes it harder to detect failures and to identify their possible root causes …
Deeptralog: Trace-log combined microservice anomaly detection through graph-based deep learning
A microservice system in industry is usually a large-scale distributed system consisting of
dozens to thousands of services running in different machines. An anomaly of the system …
dozens to thousands of services running in different machines. An anomaly of the system …
Unsupervised detection of microservice trace anomalies through service-level deep bayesian networks
The anomalies of microservice invocation traces (traces) often indicate that the quality of the
microservice-based large software service is being impaired. However, timely and …
microservice-based large software service is being impaired. However, timely and …
A survey of aiops methods for failure management
Modern society is increasingly moving toward complex and distributed computing systems.
The increase in scale and complexity of these systems challenges O&M teams that perform …
The increase in scale and complexity of these systems challenges O&M teams that perform …
Failure diagnosis in microservice systems: A comprehensive survey and analysis
Modern microservice systems have gained widespread adoption due to their high
scalability, flexibility, and extensibility. However, the characteristics of independent …
scalability, flexibility, and extensibility. However, the characteristics of independent …
Self-supervised log parsing
Logs are extensively used during the development and maintenance of software systems.
They collect runtime events and allow tracking of code execution, which enables a variety of …
They collect runtime events and allow tracking of code execution, which enables a variety of …
Autonomous selection of the fault classification models for diagnosing microservice applications
Y Song, R Xin, P Chen, R Zhang, J Chen… - Future Generation …, 2024 - Elsevier
Microservices architecture is a new approach for deploying applications and services in the
cloud, gaining popularity for constructing large-scale systems that are highly resilient, robust …
cloud, gaining popularity for constructing large-scale systems that are highly resilient, robust …
Robust multimodal failure detection for microservice systems
Proactive failure detection of instances is vitally essential to microservice systems because
an instance failure can propagate to the whole system and degrade the system's …
an instance failure can propagate to the whole system and degrade the system's …
tprof: Performance profiling via structural aggregation and automated analysis of distributed systems traces
The traditional approach for performance debugging relies upon performance profilers (eg,
gprof, VTune) that provide average function runtime information. These aggregate statistics …
gprof, VTune) that provide average function runtime information. These aggregate statistics …
TraceGra: A trace-based anomaly detection for microservice using graph deep learning
J Chen, F Liu, J Jiang, G Zhong, D Xu, Z Tan… - Computer …, 2023 - Elsevier
Trace is widely used to detect anomalies in distributed microservice systems because of the
capability of precisely reconstructing user request paths. However, most existing trace …
capability of precisely reconstructing user request paths. However, most existing trace …