Mobile resources big data deduplication and offloading algorithm based on new computing network architecture

Abstract

The big data of mobile resources under the new computing network architecture is repetitive and redundant, which leads to poor classification in the process of data scheduling and detection. In order to reduce the error rate of big data deduplication and unloading of mobile resources under the new computing network architecture, a new method of big data deduplication and unloading of mobile resources under the new computing network architecture based on redundant data elimination is proposed. Autocorrelation matched filter detection model is used to filter redundant data and suppress symbol interval interference on the prior features of mobile resource big data under the new computing network architecture with random sampling, and the clustering convergence characteristic parameters of mobile resource big data under the new computing network architecture are extracted by using sample fuzzy regression analysis and least squares sample block fusion detection method. The constrained evolution method of multi-level iterative regression analysis is used to estimate the classification features of mobile resources big data under the new computing network framework. The classification target features are input into the BP neural network classifier, and the adaptive weight distribution control of BP neural network classification is carried out by combining the adaptive clustering center optimization control algorithm, which improves the adaptability of data classification and realizes the unloading of mobile resources big data under the new computing network framework. The simulation results show that the algorithm can effectively reduce the interference of redundant data, and the fidelity rate of data classification is high and the error rate is low, which improves the dynamic management ability of mobile resource data under the new computing network architecture.