Optimized Cloud Resource Allocation Using Genetic Algorithms for Energy Efficiency and QoS Assurance
This work addresses energy efficiency and performance optimization in cloud data centers, which is an incremental improvement over existing methods.
This paper tackles the problem of dynamic resource management in cloud computing by proposing a Genetic Algorithm-based approach for Virtual Machine placement and consolidation, which reduces energy consumption by 15-20% while maintaining QoS constraints compared to traditional heuristics.
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.