EFRED: Enhancement of Fair Random Early Detection Algorithm

Muntadher Abdulkareem¹, Kassem Akil², Ali Kalakech², Seifedine Kadry³

¹Arts, Sciences and Technology University, Beirut, Lebanon.
²MIS Department, Lebanese University, Beirut, Lebanon.
³Math and Statistics Department, American University of the Middle East, Egaila, Kuwait.

DOI: 10.4236/ijcns.2015.87028                

Abstract

Quality of Service (QoS) generally refers to measurable like latency and throughput, things that directly affect the user experience. Queuing (the most popular QoS tool) involves choosing the packets to be sent based on something other than arrival time. The Active queue management is important subject to manage this queue to increase the effectiveness of Transmission Control Protocol networks. Active queue management (AQM) is an effective means to enhance congestion control, and to achieve trade-off between link utilization and delay. The de facto standard, Random Early Detection (RED), and many of its variants employ queue length as a congestion indicator to trigger packet dropping. One of these enhancements of RED is FRED or Fair Random Early Detection attempts to deal with a fundamental aspect of RED in that it imposes the same loss rate on all flows, regardless of their bandwidths. FRED also uses per-flow active accounting, and tracks the state of active flows. FRED protects fragile flows by deterministically accepting flows from low bandwidth connections and fixes several shortcomings of RED by computing queue length during both arrival and departure of the packet. Unlike FRED, we propose a new scheme that used hazard rate estimated packet dropping function in FRED. We call this new scheme Enhancement Fair Random Early Detection. The key idea is that, with EFRED Scheme change packet dropping function, to get packet dropping less than RED and other AQM algorithms like ARED, REM, RED, etc. Simulations demonstrate that EFRED achieves a more stable throughput and performs better than current active queue management algorithms due to decrease the packets loss percentage and lowest in queuing delay, end to end delay and delay variation (JITTER).

Keywords

QoS, Quality of Service, Active Queue Management, EFRED Algorithm, Fair Random Early Detection, Congestion Control

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Abbasov, B. and Korukoglu., S. (2009) An Active Queue Management Algorithm for Reducing Packet Loss Rate. Mathematical and Computational Applications, 14, 65-72.
[2]	Athuraliya, S., Li, V.H., Low, S.H. and Yin, Q. (2001) REM: Active Queue Management. IEEE Network, 15, 48-53. http://dx.doi.org/10.1109/65.923940
[3]	Balchunas, A. (2010) Introduction to QoS. Aaron Balchunas. http://www.routeralley.com/
[4]	Braden, B.A. (1998) Recommendations on Queue Management and Congestion Avoidance in the Internet. RFC 2309, April.
[5]	Chan, M.-K. and Hamdi, M. (2003) An Active Queue Management Scheme Based on a Capture-Recapture Model. IEEE Journal on Selected Areas in Communications, 21, 572-583. http://dx.doi.org/10.1109/JSAC.2003.810499
[6]	Cisco Systems Inc. (2010) Catalyst 4500 Series Switch Cisco IOS Software Configuration Guide. Cisco Press, San Jose.
[7]	Feipeng, L. (2011). Drop Tail and RED—Two AQM Mechanisms. www.roman10.net http://www.roman10.net/drop-tail-and-redtwo-aqm-mechanisms/
[8]	Feng, W.-C., Kandlur, D.D., Saha, D. and Shin, K. G. (1999) BLUE: A New Class of Active Queue Management Algorithms. Technical Report CSE-TR-387-99, Dept. of EECS, University of Michigan, April.
[9]	Floyd, S. and Jacobson, V. (1993) Random Early Detection Gateways for Congestion Avoidance. IEEE/ACM Transactions on Networking, 1, 397-413. http://dx.doi.org/10.1109/90.251892
[10]	Thiruchelvi, G. and Raja, J. (2012) Active Queue Management Based Adaptive Flow Control Mechanism for Unresponsive Flows. European Journal of Scientific Research, 70, 67-80.
[11]	Thiruchelvi, G. and Raja, J. (2008) A Survey on Active Queue Management Mechanisms. IJCSNS International Journal of Computer Science and Network Security, 8, 130-145.
[12]	Kadry, S. (2011) A New Proposed Technique to Improve Software Regression Testing Cost. arXiv preprint arXiv:1111.5640.