The fast spread of cloud computing, from specific customers to major businesses, has made it harder for cloud organizations to preserve information and services in the network infrastructure (cloud). Inefficient resource control has the potential to diminish cloud computational resources. As a result, resources should be distributed evenly to various stakeholders without endangering the organization's revenue or customer satisfaction. A client's requirement cannot be withheld endlessly simply because the basic resources are not available. In this paper, a novel integration algorithm named Ubiquitous Shuffled Leaf-frog with Whale Optimization (USLW) has been used to resolve the aforementioned issue within optimized procurement, dynamic allocation, and improved resource position in cloud computing. As a result, load optimization and balancing, energy efficacy, and improved resource scheduling are obtained in a hybrid-cloud model. The whale optimization algorithm surpassed many existing methods in form of response time, execution time, energy consumption and throughput in the case of multiple server settings to obtain better QoS in hybrid-cloud operation on the Cloud Service (CS) contributor end.

Task allocation, ubiquitous, throughput, Whale Optimization, hybrid-cloud and service provider.