Markov-Modulated Bernoulli-Based Performance Analysis for Gentle BLUE and BLUE Algorithms under Bursty and Correlated Traffic

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 GB-MMBP-2 provides the bestmean queue length, queuing time and packet loss among the compared methods.