Research on Resource Scheduling Method of Cloud Computing Based on New Grey Wolf (NGWO) Algorithm

Resource scheduling stands as a pivotal concern within the realm of cloud computing. Aiming at the defects of low resource utilization in traditional cloud computing resource scheduling, a model derived from the scenario of online education video processing platform is established. Simultaneously, a new grey wolf algorithm (NGWO) is introduced to enhance the resource scheduling strategy for cloud computing. Addressing the issue of sluggish convergence, limited global search capabilities, and susceptibility to local optima in multiple iterations of the traditional GWO, the NGWO algorithm is enhanced through chaotic mapping, improving nonlinear convergence factor and introducing dynamic weight strategy. The experiments demonstrate that the NGWO algorithm exhibits superior convergence and enhanced optimization accuracy on unimodal and multimodal functions. Furthermore, the results underscore the NGWO algorithm's heightened optimization prowess in comparison to the GWO and GWO-S algorithms.