Balanced Allocation Method of Physical Education Distance Education Resources Based on Linear Prediction

Abstract

It is simple to manufacture resource fragments, waste resources, and alter the matching impact of resource performance using the balanced allocation technique of sports distance education resources. Linear prediction is used to offer a way for distributing sports distant education resources in an equitable manner. Using linear prediction, the resource demand can be calculated, and the matching model between virtual and real resources may be constructed using the performance vectors of virtual machines and servers. The balanced allocation approach for sports distance education resources was created with the goal of lowering server count, enhancing resource utilization, and balancing the use of various resources. The balanced allocation outcome is the output Pareto optimal solution set. Its average resource performance matching distance is 765, which is 284 and 465 less than that calculated using the BF and RR algorithms for 1000 virtual machines, respectively. Therefore, in terms of matching resource performance and reducing resource fragmentation, this strategy surpasses the other two.