An Improved Framework for Power Efficiency and Resource Distribution in Cloud Computing Using Machine Learning Algorithm

Cloud computing services are available online at any time and from any location. The services for online data access, manipulation, and configuration are provided by cloud computing. The issue of cloud technology’s power usage is addressed in this paper. Methods and algorithms that could lower power usage along with allocating resources are necessary for servers to operate efficiently. Another critical component of cloud technology is load balancing, which permits balanced load distribution among numerous servers to satisfy rising client needs. The contemporary study used several optimization techniques, inclusive of the whale optimization algorithm (WOA), optimization of cat swarms (CSO), BAT-Algorithm, search algorithm for cuckoo (CSA), and particle swarm optimization (PSO), for balancing of loads, power efficiency, and more effective resource distribution to build a productive cloud environment. The consequence indicated that the whale optimization algorithm beats other algorithms in terms of response time, power usage, execution time, and throughput for the settings of seven and eight servers.