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Authors: Anouar Hilali ; Hatim Hafiddi and Zineb El Akkaoui

Affiliation: InnovatiVE REsearch on Software, Systems and daTa, Institut National des Postes et Télécommunications, Rabat, Morocco

Keyword(s): Self-adaptation, Microservices, Machine Learning.

Abstract: The Microservice architecture is increasingly becoming the preferred architecture of modern applications. The logically distinct components that make up microservices make continuous delivery easier compared to monolithic architectures. This feature however makes it difficult for engineers to control the underlying services and properly adapt them at run-time. Designing our microservices as self-adaptive systems helps us tackle this issue. Each microservice can then dynamically monitor and adapt its behavior to change certain aspects of itself to achieve self-adaptive goals. The use of statistical and Machine Learning (ML) techniques helps in this area in a lot of ways (e.g., predicting resource usage, anomaly detection, etc.). This paper aims to provide a state of the art of the use of ML in microservice adaptation, the main goal is to provide an overview of the field and identify the most frequent adaptation goals and the types of adaptation techniques used. In order to carry out a comprehensive analysis, a well-defined method of systematic mapping is performed to categorize, according to a detailed scheme, every paper relevant to this topic. The results can potentially shed light on areas where further investigation might be warranted. (More)

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Paper citation in several formats:
Hilali, A.; Hafiddi, H. and El Akkaoui, Z. (2021). Microservices Adaptation using Machine Learning: A Systematic Mapping Study. In Proceedings of the 16th International Conference on Software Technologies - ICSOFT; ISBN 978-989-758-523-4; ISSN 2184-2833, SciTePress, pages 521-532. DOI: 10.5220/0010578905210532

@conference{icsoft21,
author={Anouar Hilali. and Hatim Hafiddi. and Zineb {El Akkaoui}.},
title={Microservices Adaptation using Machine Learning: A Systematic Mapping Study},
booktitle={Proceedings of the 16th International Conference on Software Technologies - ICSOFT},
year={2021},
pages={521-532},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010578905210532},
isbn={978-989-758-523-4},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Software Technologies - ICSOFT
TI - Microservices Adaptation using Machine Learning: A Systematic Mapping Study
SN - 978-989-758-523-4
IS - 2184-2833
AU - Hilali, A.
AU - Hafiddi, H.
AU - El Akkaoui, Z.
PY - 2021
SP - 521
EP - 532
DO - 10.5220/0010578905210532
PB - SciTePress

- Science and Technology Publications, Lda.
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