Details
| Original language | English |
|---|---|
| Pages (from-to) | 2897-2922 |
| Number of pages | 26 |
| Journal | Computing |
| Volume | 106 |
| Issue number | 9 |
| Early online date | 2 Jul 2024 |
| Publication status | Published - Sept 2024 |
Abstract
Deploying virtual machines poses a significant challenge for cloud data centers, requiring careful consideration of various objectives such as minimizing energy consumption, resource wastage, ensuring load balancing, and meeting service level agreements. While researchers have explored multi-objective methods to tackle virtual machine placement, evaluating potential solutions remains complex in such scenarios. In this paper, we introduce two novel multi-objective algorithms tailored to address this challenge. The VMPMFuzzyORL method employs reinforcement learning for virtual machine placement, with candidate solutions assessed using a fuzzy system. While practical, incorporating fuzzy systems introduces notable runtime overhead. To mitigate this, we propose MRRL, an alternative approach involving initial virtual machine clustering using the k-means algorithm, followed by optimized placement utilizing a customized reinforcement learning strategy with multiple reward signals. Extensive simulations highlight the significant advantages of these approaches over existing techniques, particularly energy efficiency, resource utilization, load balancing, and overall execution time.
Keywords
- Cloud computing, Clustering, Machine learning, Virtual machine placement, 68T20, 68T42, 68T05
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Mathematics(all)
- Theoretical Computer Science
- Mathematics(all)
- Numerical Analysis
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computational Theory and Mathematics
- Mathematics(all)
- Computational Mathematics
Sustainable Development Goals
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In: Computing, Vol. 106, No. 9, 09.2024, p. 2897-2922.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Enhancing virtual machine placement efficiency in cloud data centers
T2 - a hybrid approach using multi-objective reinforcement learning and clustering strategies
AU - Ghasemi, Arezoo
AU - Toroghi Haghighat, Abolfazl
AU - Keshavarzi, Amin
N1 - Publisher Copyright: © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.
PY - 2024/9
Y1 - 2024/9
N2 - Deploying virtual machines poses a significant challenge for cloud data centers, requiring careful consideration of various objectives such as minimizing energy consumption, resource wastage, ensuring load balancing, and meeting service level agreements. While researchers have explored multi-objective methods to tackle virtual machine placement, evaluating potential solutions remains complex in such scenarios. In this paper, we introduce two novel multi-objective algorithms tailored to address this challenge. The VMPMFuzzyORL method employs reinforcement learning for virtual machine placement, with candidate solutions assessed using a fuzzy system. While practical, incorporating fuzzy systems introduces notable runtime overhead. To mitigate this, we propose MRRL, an alternative approach involving initial virtual machine clustering using the k-means algorithm, followed by optimized placement utilizing a customized reinforcement learning strategy with multiple reward signals. Extensive simulations highlight the significant advantages of these approaches over existing techniques, particularly energy efficiency, resource utilization, load balancing, and overall execution time.
AB - Deploying virtual machines poses a significant challenge for cloud data centers, requiring careful consideration of various objectives such as minimizing energy consumption, resource wastage, ensuring load balancing, and meeting service level agreements. While researchers have explored multi-objective methods to tackle virtual machine placement, evaluating potential solutions remains complex in such scenarios. In this paper, we introduce two novel multi-objective algorithms tailored to address this challenge. The VMPMFuzzyORL method employs reinforcement learning for virtual machine placement, with candidate solutions assessed using a fuzzy system. While practical, incorporating fuzzy systems introduces notable runtime overhead. To mitigate this, we propose MRRL, an alternative approach involving initial virtual machine clustering using the k-means algorithm, followed by optimized placement utilizing a customized reinforcement learning strategy with multiple reward signals. Extensive simulations highlight the significant advantages of these approaches over existing techniques, particularly energy efficiency, resource utilization, load balancing, and overall execution time.
KW - Cloud computing
KW - Clustering
KW - Machine learning
KW - Virtual machine placement
KW - 68T20
KW - 68T42
KW - 68T05
UR - http://www.scopus.com/inward/record.url?scp=85197251866&partnerID=8YFLogxK
U2 - 10.1007/s00607-024-01311-z
DO - 10.1007/s00607-024-01311-z
M3 - Article
AN - SCOPUS:85197251866
VL - 106
SP - 2897
EP - 2922
JO - Computing
JF - Computing
SN - 0010-485X
IS - 9
ER -