Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-29464
Publication type: | Conference paper |
Type of review: | Peer review (publication) |
Title: | Root cause and liability analysis in the microservices architecture for edge IoT services |
Authors: | Kalinagac, Onur Soussi, Wissem Anser, Yacine Gaber, Chrystel Gür, Gürkan |
et. al: | No |
DOI: | 10.1109/ICC45041.2023.10279721 10.21256/zhaw-29464 |
Proceedings: | ICC 2023 - IEEE International Conference on Communications |
Page(s): | 3277 |
Pages to: | 3283 |
Conference details: | IEEE International Conference on Communications (ICC), Rome, Italy, 28 May - 1 June 2023 |
Issue Date: | 2023 |
Publisher / Ed. Institution: | IEEE |
ISBN: | 978-1-5386-7462-8 |
Language: | English |
Subjects: | Anomaly detection; Root cause analysis; Microservice; Service level agreement; Causal Bayesian network |
Subject (DDC): | 004: Computer science |
Abstract: | In this work, we present a liability analysis framework for root cause analysis (RCA) in the microservices architecture with IoT-oriented containerized network services. We keep track of the performance metrics of microservices, such as service response time, memory usage and availability, to detect anomalies. By injecting faults in the services, we construct a Causal Bayesian Network (CBN) which represents the relation between service faults and metrics. Service Level Agreement (SLA) data obtained from a descriptor named TRAILS (sTakeholder Responsibility, AccountabIlity and Liability deScriptor) is also used to flag service providers which have failed their commitments. In the case of SLA violation, the constructed CBN is used to predict the fault probability of services under given metric readings and to identify the root cause. |
URI: | https://digitalcollection.zhaw.ch/handle/11475/29464 |
Fulltext version: | Accepted version |
License (according to publishing contract): | Licence according to publishing contract |
Departement: | School of Engineering |
Organisational Unit: | Institute of Computer Science (InIT) |
Published as part of the ZHAW project: | INtelligent Security and PervasIve tRust for 5G and Beyond (INSPIRE-5Gplus) |
Appears in collections: | Publikationen School of Engineering |
Files in This Item:
File | Description | Size | Format | |
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2023_Kalinagac-etal_Root-cause-liability-analysis-microservice-infrastructure_ICC2023.pdf | Accepted Version | 8.44 MB | Adobe PDF | View/Open |
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Kalinagac, O., Soussi, W., Anser, Y., Gaber, C., & Gür, G. (2023). Root cause and liability analysis in the microservices architecture for edge IoT services [Conference paper]. ICC 2023 - IEEE International Conference on Communications, 3277–3283. https://doi.org/10.1109/ICC45041.2023.10279721
Kalinagac, O. et al. (2023) ‘Root cause and liability analysis in the microservices architecture for edge IoT services’, in ICC 2023 - IEEE International Conference on Communications. IEEE, pp. 3277–3283. Available at: https://doi.org/10.1109/ICC45041.2023.10279721.
O. Kalinagac, W. Soussi, Y. Anser, C. Gaber, and G. Gür, “Root cause and liability analysis in the microservices architecture for edge IoT services,” in ICC 2023 - IEEE International Conference on Communications, 2023, pp. 3277–3283. doi: 10.1109/ICC45041.2023.10279721.
KALINAGAC, Onur, Wissem SOUSSI, Yacine ANSER, Chrystel GABER und Gürkan GÜR, 2023. Root cause and liability analysis in the microservices architecture for edge IoT services. In: ICC 2023 - IEEE International Conference on Communications. Conference paper. IEEE. 2023. S. 3277–3283. ISBN 978-1-5386-7462-8
Kalinagac, Onur, Wissem Soussi, Yacine Anser, Chrystel Gaber, and Gürkan Gür. 2023. “Root Cause and Liability Analysis in the Microservices Architecture for Edge IoT Services.” Conference paper. In ICC 2023 - IEEE International Conference on Communications, 3277–83. IEEE. https://doi.org/10.1109/ICC45041.2023.10279721.
Kalinagac, Onur, et al. “Root Cause and Liability Analysis in the Microservices Architecture for Edge IoT Services.” ICC 2023 - IEEE International Conference on Communications, IEEE, 2023, pp. 3277–83, https://doi.org/10.1109/ICC45041.2023.10279721.
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