Enhancing virtual machine placement efficiency in cloud data centers

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

verfasst von

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.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Islamic Azad University

Typ

Artikel

Journal

Computing

Band

106

Seiten

2897-2922

Anzahl der Seiten

26

ISSN

0010-485X

Publikationsdatum

09.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Theoretische Informatik, Numerische Mathematik, Angewandte Informatik, Theoretische Informatik und Mathematik, Computational Mathematics

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://doi.org/10.1007/s00607-024-01311-z (Zugang: Geschlossen)