Enhancing Energy-Resource Allocation in Cloud Environments: A Hybrid Approach Integrating Whale Optimization Algorithm

The ability to access, modify, and configure data online via the web is provided by cloud computing. The term "cloud" describes an internet network that may be accessed remotely and from any location at any time. Given that more money is invested in real, physical infrastructure than in cloud technologies, cloud computing is unquestionably an innovation. The topic of power consumption by cloud infrastructure is the focus of this paper. Algorithms and methods that can lower energy usage and schedule resources are required for servers to function effectively. Another important component of cloud computing is load balancing, which allows for the balanced distribution of load among several servers in order to meet customers' increasing demands. The current study employed a variety of optimization strategies, including Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), BAT, Cuckoo Search Algorithm (CSA) optimization algorithm, Whale optimization algorithm (WOA) and Hybrid WOABAT or load balancing, energy economy, and improved resource scheduling. The Whale optimization algorithm performed better than other algorithms in terms of response time, energy consumption, execution time, and throughput when tested with 7 and 8 servers.