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Thoth: Automatic Resource Management with Machine Learning for Container-based Cloud Platform

Topics: Cloud Application Architectures; Cloud Application Scalability and Availability; Cloud Applications Performance and Monitoring; Cloud Resource Virtualization and Composition; Cloud Workload Profiling and Migration; Microservices: Atomation Deployment and Management, Resource Allocation Elasticity, Service State and Resilience; Monitoring of Services, Quality of Service, Service Level Agreements

Authors: Akkarit Sangpetch ; Orathai Sangpetch ; Nut Juangmarisakul and Supakorn Warodom

Affiliation: King Mongkut’s Institute of Technology Ladkrabang, Thailand

Keyword(s): Cloud Computing, Scheduling, Container, Platform-as-a-Service.

Abstract: Platform-as-a-Service (PaaS) providers often encounter fluctuation in computing resource usage due to workload changes, resulting in performance degradation. To maintain acceptable service quality, providers may need to manually adjust resource allocation according to workload dynamics. Unfortunately, this approach will not scale well as the number of applications grows. We thus propose Thoth, a dynamic resource management system for PaaS using Docker container technology. Thoth automatically monitors resource usage and dynamically adjusts appropriate amount of resources for each application. To implement the automatic-scaling algorithm, we select three algorithms, namely Neural Network, Q-Learning and our rule-based algorithm, to study and evaluate. The experimental results suggest that Q-Learning can the best adapt to the load changes, followed by a rule-based algorithm and NN. With Q-Learning, Thoth can save computing resources by 28.95% and 21.92%, compared to Neural Net work and the rule-based algorithm respectively, without compromising service quality. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Sangpetch, A.; Sangpetch, O.; Juangmarisakul, N. and Warodom, S. (2017). Thoth: Automatic Resource Management with Machine Learning for Container-based Cloud Platform. In Proceedings of the 7th International Conference on Cloud Computing and Services Science - CLOSER; ISBN 978-989-758-243-1; ISSN 2184-5042, SciTePress, pages 103-111. DOI: 10.5220/0006254601030111

@conference{closer17,
author={Akkarit Sangpetch. and Orathai Sangpetch. and Nut Juangmarisakul. and Supakorn Warodom.},
title={Thoth: Automatic Resource Management with Machine Learning for Container-based Cloud Platform},
booktitle={Proceedings of the 7th International Conference on Cloud Computing and Services Science - CLOSER},
year={2017},
pages={103-111},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006254601030111},
isbn={978-989-758-243-1},
issn={2184-5042},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Cloud Computing and Services Science - CLOSER
TI - Thoth: Automatic Resource Management with Machine Learning for Container-based Cloud Platform
SN - 978-989-758-243-1
IS - 2184-5042
AU - Sangpetch, A.
AU - Sangpetch, O.
AU - Juangmarisakul, N.
AU - Warodom, S.
PY - 2017
SP - 103
EP - 111
DO - 10.5220/0006254601030111
PB - SciTePress