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A novel adaptive traffic prediction AQM algorithm

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In the Internet, network congestion is becoming an intractable problem. Congestion results in longer delay, drastic jitter and excessive packet losses. As a result, quality of service (QoS) of networks deteriorates, and then the quality of experience (QoE) perceived by end users will not be satisfied. As a powerful supplement of transport layer (i.e. TCP) congestion control, active queue management (AQM) compensates the deficiency of TCP in congestion control. In this paper, a novel adaptive traffic prediction AQM (ATPAQM) algorithm is proposed. ATPAQM operates in two granularities. In coarse granularity, on one hand, it adopts an improved Kalman filtering model to predict traffic; on the other hand, it calculates average packet loss ratio (PLR) every prediction interval. In fine granularity, upon receiving a packet, it regulates packet dropping probability according to the calculated average PLR. Simulation results show that ATPAQM algorithm outperforms other algorithms in queue stability, packet loss ratio and link utilization.

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Correspondence to Zhenyu Na.

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Na, Z., Guo, Q., Gao, Z. et al. A novel adaptive traffic prediction AQM algorithm. Telecommun Syst 49, 149–160 (2012). https://doi.org/10.1007/s11235-010-9359-2

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  • QoS
  • QoE
  • AQM
  • Congestion control
  • Traffic prediction
  • Granularity