Big Data Equilibrium Scheduling Strategy in Cloud Computing Environment

Abstract

In cloud computing environment, big data transmission channel is easily affected by multipath effect, which leads to poor channel equalization, and high bit error rate (BER) of big data transmission. In order to improve the capacity of big data transmission channel equalization scheduling in cloud computing environment, a cloud computing big data equalization scheduling algorithm is proposed based on Porter interval equalization and fuzzy C-means clustering. The big data transport channel model in cloud computing environment is constructed, and the multipath characteristics of big data transmission channel in cloud computing environment are analyzed. The big data output features are analyzed by combining similarity feature extraction and association rule mining method. The auto-correlation beamforming method is used to analyze the information clustering fusion in the big data equalization scheduling process, and the decision feedback equalization scheduling method is used to design the channel equalization of big data output in cloud computing environment. Spread spectrum technology is used to extend the big data transmission channel to improve the equalization of channel scheduling, and the big data clustering processing in cloud computing environment is combined with fuzzy C-means clustering method to realize big data equalization scheduling in cloud computing environment. The simulation results show that big data equalization scheduling in cloud computing environment has better channel equalization, strong anti-jamming ability and improved accuracy of big data classification scheduling.