AI-Driven Adaptive VM Placement Using Performance-to-Power Ratio for Sustainable Data Center Management

Abdelhadi  Amahrouch; Youssef  Saadi; Said El  Kafhali

doi:10.47852/bonviewAIA52026353

Authors

Abdelhadi Amahrouch Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Morocco https://orcid.org/0009-0009-7725-8046
Youssef Saadi Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Morocco https://orcid.org/0000-0002-1351-3935
Said El Kafhali Faculty of Sciences and Techniques, Computer, Networks, Modeling, and Mobility Laboratory, Hassan First University of Settat, Morocco https://orcid.org/0000-0001-9282-5154

DOI:

https://doi.org/10.47852/bonviewAIA52026353

Keywords:

cloud computing, VM placement, performance-to-power ratio, thermal risk, Q-Learning, thermal management, resource optimization

Abstract

Cloud data centers provide essential scalable computing resources but often suffer from inefficient resource allocation, resulting in excessive energy consumption and increased carbon emissions. This paper proposes a Q-learning-driven adaptive virtual machine placement strategy that simultaneously optimizes thermal performance and energy efficiency in heterogeneous data centers. The proposed approach explicitly considers the physical location of servers within racks as well as their processor performance-to-power ratio (PPR). By dynamically adjusting CPU utilization thresholds according to the servers’ rack positions, the method ensures that servers operate near their optimal PPR. The algorithm formally classifies servers into “best gear,” “high preferred gear,” and “low preferred gear” states. A reinforcement learning framework based on Q-Learning learns optimal placement policies to minimize energy consumption, maintain stable service-level agreements (SLAs), and reduce the risk of thermal hotspots. Experimental results show that our approach reduces energy usage by 18.43% compared to particle swarm optimization, 20% compared to genetic algorithms, and 13% compared to predictive thermal-aware cloud optimization. Furthermore, it significantly lowers SLA violations and hotspot occurrences. By improving both thermal and energy management, this work contributes to more sustainable, efficient, and environmentally responsible cloud data center operations.

Received: 3 June 2025 | Revised: 5 September 2025 | Accepted: 15 September 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest in this work.

Data Availability Statement

The data that support the findings of this study are openly available in the Microsoft Azure 2019 dataset at https://github.com/Azure/AzurePublicDataset, reference number [27].

Author Contribution Statement

Abdelhadi Amahrouch: Conceptualization, Methodology, Software, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Youssef Saadi: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Supervision, Project administration. Said El Kafhali: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision, Project administration.