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Authors: Alireza Goli 1 ; Nima Mahmoudi 1 ; Hamzeh Khazaei 2 and Omid Ardakanian 1

Affiliations: 1 University of Alberta, Edmonton, AB, Canada ; 2 York University, Toronto, ON, Canada

Keyword(s): Autoscaling, Microservices, Performance, Machine Learning.

Abstract: Microservice architecture is the mainstream pattern for developing large-scale cloud applications as it allows for scaling application components on demand and independently. By designing and utilizing autoscalers for microservice applications, it is possible to improve their availability and reduce the cost when the traffic load is low. In this paper, we propose a novel predictive autoscaling approach for microservice applications which leverages machine learning models to predict the number of required replicas for each microservice and the effect of scaling a microservice on other microservices under a given workload. Our experimental results show that the proposed approach in this work offers better performance in terms of response time and throughput than HPA, the state-of-the-art autoscaler in the industry, and it takes fewer actions to maintain a desirable performance and quality of service level for the target application.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Goli, A.; Mahmoudi, N.; Khazaei, H. and Ardakanian, O. (2021). A Holistic Machine Learning-based Autoscaling Approach for Microservice Applications. In Proceedings of the 11th International Conference on Cloud Computing and Services Science - CLOSER, ISBN 978-989-758-510-4; ISSN 2184-5042, pages 190-198. DOI: 10.5220/0010407701900198

@conference{closer21,
author={Alireza Goli. and Nima Mahmoudi. and Hamzeh Khazaei. and Omid Ardakanian.},
title={A Holistic Machine Learning-based Autoscaling Approach for Microservice Applications},
booktitle={Proceedings of the 11th International Conference on Cloud Computing and Services Science - CLOSER,},
year={2021},
pages={190-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010407701900198},
isbn={978-989-758-510-4},
issn={2184-5042},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Cloud Computing and Services Science - CLOSER,
TI - A Holistic Machine Learning-based Autoscaling Approach for Microservice Applications
SN - 978-989-758-510-4
IS - 2184-5042
AU - Goli, A.
AU - Mahmoudi, N.
AU - Khazaei, H.
AU - Ardakanian, O.
PY - 2021
SP - 190
EP - 198
DO - 10.5220/0010407701900198