# Optimized Cloud Resource Allocation Using Genetic Algorithms for Energy Efficiency and QoS Assurance

arXiv: https://arxiv.org/abs/2504.17675 · HTML: https://mlsanity.com/paper/2504.17675/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center](https://mlsanity.com/paper/2212.01896/) (2022)
- [Energy Efficient Virtual Machines Placement over Cloud-Fog Network Architecture](https://mlsanity.com/paper/2001.05091/) (2020)
- [Combination of Convolutional Neural Network and Gated Recurrent Unit for Energy Aware Resource Allocation](https://mlsanity.com/paper/2106.12178/) (2021)

## Abstract (arXiv)

Cloud computing environments demand dynamic and efficient resource management to ensure optimal performance, reduced energy consumption, and adherence to Service Level Agreements (SLAs). This paper presents a Genetic Algorithm (GA)-based approach for Virtual Machine (VM) placement and consolidation, aiming to minimize power usage while maintaining QoS constraints. The proposed method dynamically adjusts VM allocation based on real-time workload variations, outperforming traditional heuristics such as First Fit Decreasing (FFD) and Best Fit Decreasing (BFD). Experimental results show notable reductions in energy consumption, VM migrations, SLA violation rates, and execution time. A correlation heatmap further illustrates strong relationships among these key performance indicators, confirming the effectiveness of our approach in optimizing cloud resource utilization.