Hybrid Fuzzy Metaheuristic Technique for Efficient VM Selection and Migration in Cloud Data Centers

The rapid growth in cloud computing has made it essential to maintain Quality of Service (QoS) across varying workloads. Virtual machine (VM) migration plays a pivotal role in enhancing service efficiency by effectively managing resources. Yet, frequent VM migrations can lead to higher energy use and reduced performance. To tackle these issues, a novel Fuzzy-based Hybrid Optimization Algorithm, FCSFFC, which combines Fuzzy Cuckoo Search with Fuzzy Firefly Colony Optimization has been developed. The proposed method strives to reduce power usage, resource waste, computation time, and migration expenses. Simulations validate our method's superiority over existing techniques in meeting our goals. Fuzziness in the algorithm helps manage the uncertainty and vagueness in predicting workloads and resource allocation, thus making the system more flexible and robust. With fuzzy logic, our model dynamically fine-tunes migration plans according to live conditions, improving the precision of decisions. The FCSFFC model, with its dynamic threshold-assisted load prediction, has been shown to minimize performance decline, downtime, and the number of VM migrations.