Optimizing Cloud Computing Performance using Adaptive and Evolutionary Algorithms for Large Scale Applications

An increasing number of applications are straining cloud computing structures which require novel optimization strategies to boost performance measurements as well as resource utilization. The research methodology incorporates adaptive algorithms together with evolutionary algorithms which provide real-time workload adaptation and distributed system resource optimization. The project implemented machine learning models which predicted resource needs to trigger automatic parameter modification for computing systems. The three major cloud platforms provided results which showed a 37.8% decrease in response latency and a 42.3% lower resource wastage rate and a 29.5% enhanced throughput capacity than static allocation techniques. The cost efficiency rose 31.7% while service reliability maintained 99.4% during the period. A proposed adaptable-evolutionary framework provides an effective solution that leads to improved performance measurements of large-scale cloud programs while reducing operational expenses for transitioning between dynamic computing settings.