loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.144.4.54

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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, SciTePress, 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
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic