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Optimization of Anomaly Detection in a Microservice System Through Continuous Feedback from Development

Hrusto, Adha LU orcid ; Engström, Emelie LU orcid and Runeson, Per LU orcid (2022) 2022 IEEE/ACM 10th International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems (SESoS) p.13-20
Abstract
Monitoring a microservice system may bring a lot of benefits to development teams such as early detection of run-time errors and various performance anomalies. In this study, we explore deep learning (DL) solutions for detection of anomalous system's behavior based on collected monitoring data that consists of applications' and systems' performance metrics. The study is conducted in a collaboration with a Swedish company responsible for ticket and payment management in public transportation. Moreover, we specifically address a shortage of approaches for evaluating DL models without any ground truth data. Hence, we propose a solution design for anomaly detection and reporting alerts inspired by state-of-the-art DL solutions. Furthermore, we... (More)
Monitoring a microservice system may bring a lot of benefits to development teams such as early detection of run-time errors and various performance anomalies. In this study, we explore deep learning (DL) solutions for detection of anomalous system's behavior based on collected monitoring data that consists of applications' and systems' performance metrics. The study is conducted in a collaboration with a Swedish company responsible for ticket and payment management in public transportation. Moreover, we specifically address a shortage of approaches for evaluating DL models without any ground truth data. Hence, we propose a solution design for anomaly detection and reporting alerts inspired by state-of-the-art DL solutions. Furthermore, we propose a plan for its in-context implementation and evaluation empowered by feedback from the development team. Through continuous feedback from development, the labeled data is generated and used for optimization of the DL model. In this way, a microservice system may leverage DL solutions to address rising challenges within its architecture. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
IEEE/ACM 10th International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems (SESoS 2022)
pages
13 - 20
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2022 IEEE/ACM 10th International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems (SESoS)
conference location
Pittsburgh, PA, United States
conference dates
2022-05-16 - 2022-05-16
external identifiers
  • scopus:85135201073
ISBN
978-1-4503-9334-8
978-1-6654-6238-9
DOI
10.1145/3528229.3529382
project
Continuous system testing using autonomous monitors
language
English
LU publication?
yes
id
e024c495-ee65-4f3a-a167-0bd0aff6c9c3
alternative location
https://ieeexplore.ieee.org/document/9808684
date added to LUP
2022-08-15 10:31:49
date last changed
2024-06-26 08:38:36
@inproceedings{e024c495-ee65-4f3a-a167-0bd0aff6c9c3,
  abstract     = {{Monitoring a microservice system may bring a lot of benefits to development teams such as early detection of run-time errors and various performance anomalies. In this study, we explore deep learning (DL) solutions for detection of anomalous system's behavior based on collected monitoring data that consists of applications' and systems' performance metrics. The study is conducted in a collaboration with a Swedish company responsible for ticket and payment management in public transportation. Moreover, we specifically address a shortage of approaches for evaluating DL models without any ground truth data. Hence, we propose a solution design for anomaly detection and reporting alerts inspired by state-of-the-art DL solutions. Furthermore, we propose a plan for its in-context implementation and evaluation empowered by feedback from the development team. Through continuous feedback from development, the labeled data is generated and used for optimization of the DL model. In this way, a microservice system may leverage DL solutions to address rising challenges within its architecture.}},
  author       = {{Hrusto, Adha and Engström, Emelie and Runeson, Per}},
  booktitle    = {{IEEE/ACM 10th International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems (SESoS 2022)}},
  isbn         = {{978-1-4503-9334-8}},
  language     = {{eng}},
  month        = {{05}},
  pages        = {{13--20}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Optimization of Anomaly Detection in a Microservice System Through Continuous Feedback from Development}},
  url          = {{http://dx.doi.org/10.1145/3528229.3529382}},
  doi          = {{10.1145/3528229.3529382}},
  year         = {{2022}},
}