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Improved Flow Awareness Among Edge Nodes by Learning-Based Sampling in Software Defined Networks

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To improve the specific quality of service, internal network management and security analysis in the future mobile network, accurate flow-awareness in the global network through packet sampling has been a viable solution. However, the current traffic measurement method with the five tuples cannot recognize the deep information of flows, and the Deep Packet Inspection (DPI) deployed at the gateways or access points is lack of traffic going through the internal nodes(e.g., base station, edge server). In this paper, by means of Deep Q-Network (DQN) and Software-Defined Networking (SDN) technique, we propose a flow-level sampling framework for edge devices in the Mobile Edge Computing (MEC) system. In the framework, an original learning-based sampling strategy considering the iterative influences of nodes is used for maximizing the long-term sampling accuracy of both mice and elephant flows. We present an approach to effectively collect traffic packets generated from base stations and edge servers in two steps: 1) adaptive node selection, and 2) dynamic sampling duration allocation by Deep Q-Learning. The results show that the approach can improve the sampling accuracy, especially for mice flows.

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This work is partially supported by the National Key R&D Program of China (2018YFC0809803), China NSFC (Youth) through grant 61702364, China NSFC GD Joint fund U1701263.

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Correspondence to Xiaofei Wang.

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Deng, J., Cai, H., Chen, S. et al. Improved Flow Awareness Among Edge Nodes by Learning-Based Sampling in Software Defined Networks. Mobile Netw Appl 27, 1867–1879 (2022).

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