Open Access Open Access  Restricted Access Subscription or Fee Access

LALRED Algorithm for Congestion Avoidance in Wired Networks

P. Muthumari, E. Manohar

Abstract


Some applications such as audio and video conferencing require a network to provide QoS guarantee. End- 2-End delay is one of the prominent factors in QoS. Packets after crossing the routers queue arrive to destination node. Thus with guaranteeing the queuing delay in routers the network will be able to guarantee End-2-End delay. Furthermore developers can contract service level agreement (SLA) intelligently. In order to guarantee queuing delay, congestion control algorithms can be used in routers. Furthermore providers can contract service level agreement (SLA) intelligently. Congestion control algorithms are a solution to guarantee queuing delay. A learning automata (LA) is an automaton that interacts with a random environment, having as its goal the task of learning the optimal action based on its acquired experience. Here , we present a LALRED algorithm for congestion avoidance in wired networks. The main aim of this algorithm is to optimize the value of the average size of the queue used for congestion avoidance and to consequently reduce the total loss of packets at the queue and also reduces the Queue delay. We achieve this by applying the LA algorithm at the gateways and by discretizing the probabilities of the corresponding actions of the congestion-avoidance algorithm. In Every Iteration the LALRED, chooses the action which is having the highest estimate vector. This algorithm reduces the number of packet losses at the gateway and also reduces the queue delay.

Keywords


Average Queue Size, Discretized Pursuit Reward Inaction, Random Early Detection (RED), Stochastic Learning Automata (LA), Queue Delay.

Full Text:

PDF

References


M. Agache, “Families of estimator-based stochastic learning algorithms,” M.S. thesis, School Comput. Sci., Carleton Univ., Ottawa, ON, Canada, 2000.

M. Agache and B. J. Oommen, “Continuous and discretized generalized pursuit learning schemes,” in Proc. 4th SCI, S. Andraddttir, K. J. Healy, D. H. Withers, and B. L. Nelson, Eds., Orlando, FL, Jul. 2000, pp. VII:270–VII:275.

M. Agache and B. J. Oommen, “Generalized pursuit learning schemes: New families of continuous and discretized learning automata,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 32, no. 6, pp. 738–749, Dec. 2002.

J. Antila, TCP Performance Simulations Using NS2, Last accessed on Feb. 3, 2009. [Online]. Available:http://www.netlab.tkk.fi/j˜mantti3/pubs/special study.pdf

R. Fengyuan, R. Yong, and S. Xiuming, “Enhancement to RED algorithm,” in Proc. 9th IEEE Int. Conf. Netw., Oct. 2001, pp. 14–19.

M. Agache, “Families of estimator-based stochastic learning algorithms,” M.S. thesis, School Comput. Sci., Carleton Univ., Ottawa, ON, Canada, 2000.

M. Agache and B. J. Oommen, “Continuous and discretized generalized pursuit learning schemes,” in Proc. 4th SCI, S. Andraddttir, K. J. Healy, D. H. Withers, and B. L. Nelson, Eds., Orlando, FL, Jul. 2000, pp. VII:270–VII:275.

M. Agache and B. J. Oommen, “Generalized pursuit learning schemes: New families of continuous and discretized learning automata,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 32, no. 6, pp. 738–749, Dec. 2002.

J. Antila, TCP Performance Simulations Using NS2, Last accessed on Feb. 3, 2009. [Online]. Available:http://www.netlab.tkk.fi/j˜mantti3/pubs/special study.pdf

R. Fengyuan, R. Yong, and S. Xiuming, “Enhancement to RED algorithm,” in Proc. 9th IEEE Int. Conf. Netw., Oct. 2001, pp. 14–19.

M. Agache, “Families of estimator-based stochastic learning algorithms,” M.S. thesis, School Comput. Sci., Carleton Univ., Ottawa, ON, Canada, 2000.

M. Agache and B. J. Oommen, “Continuous and discretized generalized pursuit learning schemes,” in Proc. 4th SCI, S. Andraddttir, K. J. Healy, D. H. Withers, and B. L. Nelson, Eds., Orlando, FL, Jul. 2000, pp. VII:270–VII:275.

M. Agache and B. J. Oommen, “Generalized pursuit learning schemes: New families of continuous and discretized learning automata,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 32, no. 6, pp. 738–749, Dec. 2002.

J. Antila, TCP Performance Simulations Using NS2, Last accessed on Feb. 3, 2009. [Online]. Available:http://www.netlab.tkk.fi/j˜mantti3/pubs/special study.pdf

R. Fengyuan, R. Yong, and S. Xiuming, “Enhancement to RED algorithm,” in Proc. 9th IEEE Int. Conf. Netw., Oct. 2001, pp. 14–19.

S. Floyd and V. Jacobson, “Random Early Detection gateways for con-gestion avoidance,” IEEE/ACM Trans. Netw., vol. 1, no. 4, pp. 397–413, Aug. 1993.

J. K. Lanctôt and B. J. Oommen, “Discretized estimator learning automata,” IEEE Trans. Syst., Man, Cybern., vol. 22, no. 6, pp. 1473–1483, Nov./Dec. 1992.

Active Queue Management, Last accessed on Feb. 3,2009.[Online].Available:http://en.wikipedia.org/wiki/active queue management.

S. Misra and B. J. Oommen, “GPSPA: A new adaptive algorithm for maintaining shortest path routing trees in stochastic networks,” Int. J. Commun. Syst., vol. 17, no. 10, pp. 963–984, Dec. 2004.

M. A. L. Thathachar and B. J. Oommen, “Discretized reward inaction learning automata,” J. Cybern. Inf. Sci., vol. 2, no. 1, pp. 24-29, Spring 1979.

B. J. Oommen and M. Agache, “Continuous and discretized pursuit learning schemes: Various algorithms and their comparison,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 31, no. 3,pp. 277– 287,june 2001

B. J. Oommen and J. P. R. Christensen, “ε-optimal discretized reward–penalty learning automata,” IEEE Trans. Syst., Man, Cybern. vol.18,no. 3, pp. 451–457, May/Jun. 1988.

NS2 Network Simulator, Last accessed on Feb. 3, 2009.Available:http://www.isi.edu/nsnam/ns/

Sudip Misra, B. J.Oommen “Random Early Detection for Congestion Avoidance in Wired Networks: A Discretized Pursuit Learning-Automata-Like Solution,” IEEE Trans. Syst., Man, Cybern. B, vol. 40, no.1,pp. 66–76, Feb 2010.

M.Jahanshahi, M.R.Meybodi “An Adaptive Congestion Control Method for Guaranteeing Queuing Delay in RED-Based Queue Using Learning Automata” IEEE Int. Conf. on Mechatronics and Automation., pp.3360-3365, Aug 2007.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.