Abstract
Microservice architectures are increasingly adopted to design large-scale applications. However, the highly distributed nature and complex dependencies of microservices complicate automatic performance diagnosis and make it challenging to guarantee service level agreements (SLAs). In particular, identifying the culprits of a microservice performance issue is extremely difficult as the set of potential root causes is large and issues can manifest themselves in complex ways. This paper presents an application-agnostic system to locate the culprits for microservice performance degradation with fine granularity, including not only the anomalous service from which the performance issue originates but also the culprit metrics that correlate to the service abnormality. Our method first finds potential culprit services by constructing a service dependency graph and next applies an autoencoder to identify abnormal service metrics based on a ranked list of reconstruction errors. Our experimental evaluation based on injection of performance anomalies to a microservice benchmark deployed in the cloud shows that our system achieves a good diagnosis result, with 92% precision in locating culprit service and 85.5% precision in locating culprit metrics.
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Notes
- 1.
Sock-shop - https://microservices-demo.github.io/.
- 2.
Google Cloud Engine - https://cloud.google.com/compute/.
- 3.
Istio - https://istio.io/.
- 4.
Node-exporter - https://github.com/prometheus/node_exporter.
- 5.
Cadvisor - https://github.com/google/cadvisor.
- 6.
Prometheus - https://prometheus.io/.
- 7.
stress-ng - https://kernel.ubuntu.com/~cking/stress-ng/.
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Acknowledgment
This work is part of the FogGuru project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 765452. The information and views set out in this publication are those of the author(s) and do not necessarily reflect the official opinion of the European Union. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein.
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Wu, L., Bogatinovski, J., Nedelkoski, S., Tordsson, J., Kao, O. (2021). Performance Diagnosis in Cloud Microservices Using Deep Learning. In: Hacid, H., et al. Service-Oriented Computing – ICSOC 2020 Workshops. ICSOC 2020. Lecture Notes in Computer Science(), vol 12632. Springer, Cham. https://doi.org/10.1007/978-3-030-76352-7_13
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