Enhancing virtual machine placement efficiency in cloud data centers

a hybrid approach using multi-objective reinforcement learning and clustering strategies

authored by
Arezoo Ghasemi, Abolfazl Toroghi Haghighat, Amin Keshavarzi
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.

Organisation(s)
L3S Research Centre
External Organisation(s)
Islamic Azad University
Type
Article
Journal
Computing
Volume
106
Pages
2897-2922
No. of pages
26
ISSN
0010-485X
Publication date
09.2024
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Software, Theoretical Computer Science, Numerical Analysis, Computer Science Applications, Computational Theory and Mathematics, Computational Mathematics
Sustainable Development Goals
SDG 7 - Affordable and Clean Energy
Electronic version(s)
https://doi.org/10.1007/s00607-024-01311-z (Access: Closed)