A multiobjective metaheuristic-based container consolidation model for cloud application performance improvement
- Author
- Vincent Bracke (UGent) , José Pedro Pereira dos Santos (UGent) , Tim Wauters (UGent) , Filip De Turck (UGent) and Bruno Volckaert (UGent)
- Organization
- Project
- Abstract
- This work describes an approach to enhance container orchestration platforms with an autonomous and dynamic rescheduling system that aims at improving application service time by co-locating highly interdependent containers for network delay reduction. Unreasonable container consolidation may however lead to host CPU saturation, in turn impairing the service time. The multiobjective approach proposed in this work aims to improve application service-time by minimizing both inter-server network traffic and CPU throttling on overloaded servers. To this extent, the Simulated Annealing combinatorial optimization heuristic is used and compared on its relative performance towards the optimal solution obtained by Mathematical Programming. Additionally, the impact of the proposed system is validated on a Kubernetes cluster hosting three concurrent applications, and this under varying load scenarios. The proposed rescheduling system systematically i) improves the application service-time (up to 27.2% from our experiments) and ii) surpasses the improvement reached by the Kubernetes descheduler.
- Keywords
- OPTIMIZATION, ORCHESTRATION, SCHEME, Cloud computing services, Kubernetes, Container scheduling, Mathematical optimization, Autonomic and cognitive management, Performance management
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01J179H04EWB6Z42RZ1KEP43JS
- MLA
- Bracke, Vincent, et al. “A Multiobjective Metaheuristic-Based Container Consolidation Model for Cloud Application Performance Improvement.” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, vol. 32, no. 3, 2024, doi:10.1007/s10922-024-09835-7.
- APA
- Bracke, V., Pereira dos Santos, J. P., Wauters, T., De Turck, F., & Volckaert, B. (2024). A multiobjective metaheuristic-based container consolidation model for cloud application performance improvement. JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, 32(3). https://doi.org/10.1007/s10922-024-09835-7
- Chicago author-date
- Bracke, Vincent, José Pedro Pereira dos Santos, Tim Wauters, Filip De Turck, and Bruno Volckaert. 2024. “A Multiobjective Metaheuristic-Based Container Consolidation Model for Cloud Application Performance Improvement.” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 32 (3). https://doi.org/10.1007/s10922-024-09835-7.
- Chicago author-date (all authors)
- Bracke, Vincent, José Pedro Pereira dos Santos, Tim Wauters, Filip De Turck, and Bruno Volckaert. 2024. “A Multiobjective Metaheuristic-Based Container Consolidation Model for Cloud Application Performance Improvement.” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 32 (3). doi:10.1007/s10922-024-09835-7.
- Vancouver
- 1.Bracke V, Pereira dos Santos JP, Wauters T, De Turck F, Volckaert B. A multiobjective metaheuristic-based container consolidation model for cloud application performance improvement. JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT. 2024;32(3).
- IEEE
- [1]V. Bracke, J. P. Pereira dos Santos, T. Wauters, F. De Turck, and B. Volckaert, “A multiobjective metaheuristic-based container consolidation model for cloud application performance improvement,” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, vol. 32, no. 3, 2024.
@article{01J179H04EWB6Z42RZ1KEP43JS,
abstract = {{This work describes an approach to enhance container orchestration platforms with an autonomous and dynamic rescheduling system that aims at improving application service time by co-locating highly interdependent containers for network delay reduction. Unreasonable container consolidation may however lead to host CPU saturation, in turn impairing the service time. The multiobjective approach proposed in this work aims to improve application service-time by minimizing both inter-server network traffic and CPU throttling on overloaded servers. To this extent, the Simulated Annealing combinatorial optimization heuristic is used and compared on its relative performance towards the optimal solution obtained by Mathematical Programming. Additionally, the impact of the proposed system is validated on a Kubernetes cluster hosting three concurrent applications, and this under varying load scenarios. The proposed rescheduling system systematically i) improves the application service-time (up to 27.2% from our experiments) and ii) surpasses the improvement reached by the Kubernetes descheduler.}},
articleno = {{61}},
author = {{Bracke, Vincent and Pereira dos Santos, José Pedro and Wauters, Tim and De Turck, Filip and Volckaert, Bruno}},
issn = {{1064-7570}},
journal = {{JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT}},
keywords = {{OPTIMIZATION,ORCHESTRATION,SCHEME,Cloud computing services,Kubernetes,Container scheduling,Mathematical optimization,Autonomic and cognitive management,Performance management}},
language = {{eng}},
number = {{3}},
pages = {{45}},
title = {{A multiobjective metaheuristic-based container consolidation model for cloud application performance improvement}},
url = {{http://doi.org/10.1007/s10922-024-09835-7}},
volume = {{32}},
year = {{2024}},
}
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