The Control Method of Big Data Information Flow Based on Semantic Characteristics in Cloud Computing Environment

Abstract

The control of local network information flow can effectively improve the real-time and smooth transmission of network information. Therefore, a big data information flow control method based on semantic features in the cloud computing environment is proposed. In the cloud computing environment, by calculating the network big data information frame size, calculating the data frame rate adjustment series, according to the detected big data information flow rate and transmission rate, to ensure that the big data information flow transmission rate is not less than the frame rate. The big data information flow is dynamically corrected and hierarchical controlled. According to the semantic feature extraction coefficient obtained by decomposition, a threshold is selected to reconstruct the original signal of the big data information flow and remove the communication interference of the big data information flow. Referring to the idea of network link weight, when balancing network congestion, the load of each big data information flow is balanced according to the bandwidth occupancy ratio of each big data information flow, and the load of each sub flow is balanced by setting the network bandwidth occupancy ratio parameter. By setting the network bandwidth occupancy ratio parameter of big data information flow, the load of each sub flow is balanced, and the real-time control of big data information flow is realized. Experimental results show that the big data information flow control method based on semantic features in the cloud computing environment can not only reduce the noise content of big data information flow, but also improve the control speed of big data information flow, with better control performance.