Research on Network Resource Collection and Distribution Strategies in Big Data-Driven Multimedia Teaching - Jianping Han (Advances in Multimedia)
The multimedia teaching information data generated in the era of big data are increasing at a huge scale, but the computing environment used before is difficult to meet users’ requirements for data integration. Multimedia information has the characteristics of diversity and structure diversity, which lead to the situation that the storage and processing methods are too complicated and time-consuming to varying degrees. This brings great challenges to big data analysis. Therefore, the fusion of this part of the data information requires a large-scale data parallel framework environment to ensure that multiple data calculation instructions can be executed each time, improve the range of problem solving, and essentially improve the complexity of multimedia teaching information. The main index of large-scale data fusion is computing speed. Aiming at the performance defects of the open source computing framework Hadoop, this paper intends to establish a new computing model. This model uses distributed data sets to replace the previous data structures and solve some problems in the process of data integration, thereby significantly improving the computation efficiency and reducing resource usage. Starting from the network’s perception of energy resources and considering that the randomness of energy available to nodes in the energy harvesting cognitive relay different systems will lead to different data volumes such as throughput and resource utilization, this paper takes the network’s throughput as the optimization network object for resource collection and allocation research. Experiment description: compared with the previous resource management, this model can reduce the total consumption of the system; compared with the single base station mode, the proposed method can extend the network life cycle to a greater extent; meanwhile, it ensures the use of users. Based on the stability analysis of the data, an optimal solution is obtained that can maximize the stable throughput of cognitive users and effectively improve resource collection and allocation.