Cloud computing model for big data processing and performance optimization of multimedia communication

In this paper, the technical aspects of big data processing of multimedia communication are deeply studied. Firstly, we propose a cloud implementation method of radial basis function neural network which is based on Map-Reduce on cloud computing cluster. Secondly, in order to meet the needs of big data processing of multimedia communication, the map-Reduce-based error back-propagation algorithm is trained to effectively map the effective mapping mechanism of multi-layer neural networks. For a cloud algorithm on a cloud computing cluster and a serial algorithm on a single processor, the time required to implement the algorithm is theoretically derived, and the cloud algorithm and performance parameters (acceleration ratio) on the cloud cluster are evaluated, the optimal number and minimum number of data nodes). Finally, the experimental results show that compared with the existing algorithms, the cloud algorithm proposed in this paper has better acceleration speed, faster convergence speed and fewer iterations.