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

2025/04/24 by Caroline Panggabean, Panggabean, Caroline, Devaraj Verma C +5
Computer Science · #Artificial Intelligence (cs.AI) #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2504.17675

openalex publication_date 2025/04/24 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/28

Abstract

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.

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