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An AI-Based Adaptive Cognitive Modeling and Measurement Method of Network Traffic for EIS

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Abstract

Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61571104), Sichuan Science and Technology Program (No. 2018JY0539), Key projects of the Sichuan Provincial Education Department (No. 18ZA0219), Fundamental Research Funds for the Central Universities (No. ZYGX2017KYQD170), Shandong Provincial Natural Science Foundation (ZR2017QF015), and Innovation Funding (No. 2018510007000134). The authors wish to thank the reviewers for their helpful comments. Dr. Dingde Jiang is corresponding author of this paper (email: jiangdd@uestc.edu.cn).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Liuwei Huo

  2. School of Astronautics and Aeronautics, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Dingde Jiang & Sheng Qi

  3. Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL, 32114, USA

    Houbing Song

  4. Mechatronics Engineering, Middle Tennessee State University, Murfreesboro, TN, 37132, USA

    Lei Miao

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  1. Liuwei Huo
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  2. Dingde Jiang
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  3. Sheng Qi
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  4. Houbing Song
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  5. Lei Miao
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Correspondence to Dingde Jiang.

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Huo, L., Jiang, D., Qi, S. et al. An AI-Based Adaptive Cognitive Modeling and Measurement Method of Network Traffic for EIS. Mobile Netw Appl 26, 575–585 (2021). https://doi.org/10.1007/s11036-019-01419-z

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