A Systematic Approach to Enhancing Cloud Performance Using GA-Driven Adaptive Resource Allocation Strategies

In the last ten years, cloud computing has become a prominent a study providing scalable computing power through the Internet. As a consequence of extensive adoption and simultaneous increase in demand, a cloud environment often experiences burst loads. Therefore algorithms must be devised for efficient load balancing that works in real time while dynamically adjusting itself to any fluctuations in workload. Of these techniques, Genetic Algorithms (GAs) inspired by the processes of natural evolution have become prominent. Such algorithms use the process of natural selection to generate, combine and improve solutions for performance optimization. Researchers are continuously investigating these different GA-based techniques in order to assess their effectiveness, pinpoint the drawbacks, and assess their influence on key performance indicators like response time, resource utilization, energy consumption, and overall system throughput. Within this scope, an enhanced and new hybrid model combining Adaptive Resource Allocation with Genetic Algorithm has been proposed for better load balancing