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

Caroline Panggabean Department of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka carolinepgabean@gmail.com ORCID: https://orcid.org/0009 - 0004 - 9964 - 7986 Ranju Limbu Department of CSE (AIM) JAIN (Deemed - to - be University) Bangalore, Karnataka 21btlca002 @jainuniversity.ac.in Dr. Devaraj Verma C Department of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka c.devaraj@jainuniversity.ac.in ORCID: https://orcid.org/0000 - 0002 - 1504 - 4263 Rhythm Sarker Department of CSE (AIML) JAIN (Deemed - to - be University) Bangalore, Karnataka 21btrca065 @jainuniversity.ac.in Bhagyashree Gogoi Department of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka 21btlca001 @ jainuniver s ity.ac.in 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 consol idation, 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 De creasing (BFD). Experimental results show notable reductions in energy consumption, VM migrations, SLA violation rates, and execution time.