Research Article Open Access

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

Adeeb Alsaaidah1, Mohd Zalisham1, Mohd Fadzli1 and Hussein Abdel-Jaber2
  • 1 Universiti Sains Islam Malaysia (USIM), Malaysia
  • 2 Arab Open University, Saudi Arabia
Journal of Computer Science
Volume 12 No. 6, 2016, 289-299

DOI: https://doi.org/10.3844/jcssp.2016.289.299

Submitted On: 23 August 2015 Published On: 30 June 2016

How to Cite: Alsaaidah, A., Zalisham, M., Fadzli, M. & Abdel-Jaber, H. (2016). Markov-Modulated Bernoulli-Based Performance Analysis for Gentle BLUE and BLUE Algorithms under Bursty and Correlated Traffic. Journal of Computer Science, 12(6), 289-299. https://doi.org/10.3844/jcssp.2016.289.299

Abstract

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.

  • 1,059 Views
  • 1,141 Downloads
  • 1 Citations

Download

Keywords

  • Congestion Control
  • Queue Management
  • Markov Modulated Bernoulli Process
  • Gentle BLUE
  • Performance Evaluation