摘要
The network traffic prediction is important for service quality control in computer network.The performance of the traditional prediction method significantly degrades for the burst short-term flow.In view of the problem,this paper proposes a double LSTMs structure,one of which acts as the main flow predictor,another as the detector of the time the burst flow starts at.The two LSTM units can exchange information about their internal states,and the predictor uses the detector’s information to improve the accuracy of the prediction.A training algorithm is developed specially to train the structure offline.To obtain the prediction online,a pulse series is used as a simulant of the burst event.A simulation experiment is designed to test performance of the predictor.The results of the experiment show that the prediction accuracy of the double LSTM structure is significantly improved,compared with the traditional single LSTM structure.
出处
《国际计算机前沿大会会议论文集》
2020年第1期380-388,共9页
International Conference of Pioneering Computer Scientists, Engineers and Educators(ICPCSEE)
基金
the research plan of State Grid Sichuan Electric Power Company,China
the research plan of the 10th Research Institute of China Electronics Technology Group Corporation(KTYT-XY-002).