In this paper, early stage congestion control method at the router buffer is proposed. The proposed method, namely, Gentle BLUE (GB), extends the well-known BLUE method by providing a dynamic mechanism for calculating the dropping probability based on the status of the queue length. This dynamic-based overcomes the slow-to-congestion response shortage in the existing methods and provides better management for the router buffer. Unlike the original BLUE method, which has a slow response when sudden heavy congestion occurs as it uses fixed Pinc and Pdec values to adjust the dropping probability, the proposed method predicts congestion before it occurs and responds using dynamically updated values. The dropping value is dynamically calculated based on queue length and the remaining buffer capacity. The proposed GB method is simulated and the obtained results are compared with the existing Active Queue Management (AQM) methods namely, Random Early Detection (RED), Adaptive Gentle Random Early Detection (AGRED) and (BLUE) method. The comparison is conducted in terms of mean queue length (mql), queuing waiting time (D), throughput (T), packet loss (PL) and dropping probability (DP). The results show that the proposed method provides better queue length, better delayed queuing, and packet loss probability compared with the existing methods. � 2014 IEEE.

This paper presents a performance study for Gentle BLUE (GB) under the bursty and correlated properties of aggregated network traffic. The Bernoulli Process (BP) fails to represent the properties of aggregated correlated and bursty traffic, so instead of that, MMBP has been used. MMBP is A 2D discrete-time Markov chain modeling for GB algorithm with two traffic classes, each with its own parameters. The proposed model is compared with the GB that uses the BP as a source model (GB-BP) and original BLUE that uses the BP (BLUE-BP) and MMBP (BLUE-MMBP-2) as source model. The evaluation is conducted in term of queuing waiting time, mean queue length, throughput, packet loss and dropping probability. When congestion (e.g., heavy congestion) occurs, the results show that GBMMBP- 2 provides the bestmean queue length, queuing time and packet loss among the compared methods. � 2016 Adeeb Alsaaidah, Mohd Zalisham, Mohd Fadzli and Hussein Abdel-Jaber.