Congestion control mechanism for ATM networks using neural networks

Tarraf, A.A.; Habib, I.; Saadawi, Tarek

doi:10.1109/icc.1995.525166

Cited by 17 publications

(13 citation statements)

References 8 publications

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“…Small changes in the existing packets of network management protocol results a good non-central coordination management. This technique is widely used to reduce congestion in Asynchronous Transfer Mode (ATM) networks [5]. ATM networks are widely accepted communication mechanism for organizations.…”

Section: Introductionmentioning

confidence: 99%

Novel Fair Share Flow scheduling algorithm for ATM network

Kizhakkethottam

Karthik²,

Chandra

2013

2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

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ATM networks have many paths linking each host pair to achieve high bisection bandwidth for arbitrary communication patterns. Completely exploiting the bisection bandwidth may require flows between the same source and destination pair to take dissimilar paths to avoid hot spots. Though, the prevailing routing protocols have little support for load-sensitive adaptive routing [1]. This work proposes Fair Share Flow scheduling architecture for ATM networks. Fair Share Flow scheduling allows each end host to regulate traffic from overloaded paths to under loaded ones deprived of central coordination. We use open flow implementation and simulations to show that Fair Share Flow scheduling can efficiently use the network's bisection bandwidth. It outperforms prior solutions based on random flow-level scheduling, and performs correspondingly to previous work that assigns flows to paths using a centralized scheduler but deprived of its scaling limitation. We are using competitive game theory[3] to show that Fair Share Flow scheduling algorithm is more constant [2]. It makes growth in every step and converges to Nash equilibrium in finite steps.

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Section: Introductionmentioning

confidence: 99%

Novel Fair Share Flow scheduling algorithm for ATM network

Kizhakkethottam

Karthik²,

Chandra

2013

2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

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“…Call admission control [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] means that an ATM node allows a user to start a new call only when the node judges that the QoS does not degrade the QoS required by all of the users already connected. Bandwidth control also is a very important function in ATM network.…”

Section: Call Connection Control (Cac)mentioning

confidence: 99%

“…The controller used the offering traffic characteristics, QoS requirement and actual network performance to decide accept or reject this call. Tarraf [11] used performance function in neural networks. The objective is to 1) minimize the cell loss rate (CLR), which can control the traffic congestion in ATM network; 2) preserve the quality of current service via maintain the coding rate of its source.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, neural networks have been used widely to deal with traffic-control-related problems in ATM networks [4][5][6][7][8][9][10][11]. Hiramatsu [4] proposed a call connection admission controller based on neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…Asynchronous Transfer Mode (ATM) is a key technology for the broadband integrated services digital network (B-ISDN), which is designed to efficiently support high speed digital voice, video, image and data services [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. ATM is a connection-oriented packet switching technology.…”

Section: Introductionmentioning

confidence: 99%

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Neural network training using genetic algorithms in ATM traffic control

Bourbakis²

Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)

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There are various traditional mathematical approaches used in ATM traffic control to maintain the QoS. However, most of these approaches are not suitable for handling the wide variety of ATM services and diversity of their combinations. The precise characteristics of the source traffic are not known and the service quality requirements change over time. Building an efficient network controller which can control the network traffic is a difficult task. The advantage of using NNs in ATM is that the QoS can be accurately estimated without detailed user action models or knowledge about the switching system architecture. The disadvantage is that it will take longer time to train with ATM network changes. In this paper, we use Genetic Algorithms in Neural Network weights training for ATM Call Admission Control (CAC) and Usage Parameter Control (UPC). The simulation results have shown not only a guarantee for the QoS of all the services, but also a saving of the system bandwidth and an improvement of the throughput.

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AI Algorithms in Networks

2022

Artificial Intelligence and Quantum Computing for Advanced Wireless Networks

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Congestion control mechanism for ATM networks using neural networks

Cited by 17 publications

References 8 publications

Novel Fair Share Flow scheduling algorithm for ATM network

Novel Fair Share Flow scheduling algorithm for ATM network

Neural network training using genetic algorithms in ATM traffic control

AI Algorithms in Networks

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