Congestion Control Framework in Ad-Hoc Wireless using Neural Networks in QoS

Keshav Jindal, Surjeet Dalal, Gundeep Tanwar

Abstract


A Congestion control is concerned with allocating the resources in a network such that the network can operate at an acceptable performance level when the demand exceeds or is near the capacity of the network resources. The rapid growth of the Internet and increased demand to use the Internet for time-sensitive voice and video applications necessitate the design and utilization of effective congestion control algorithms. In order to provide these bounds led to the development of alternate mechanisms based on policies that could be deployed on the existing network infrastructure with minimal changes, and still provide a suitable Quality of Service. Differentiated Services (Diff-Serv) is one such technology. DS can be implemented with relatively small disturbance to the existing infrastructure. The networks using these services are called Differentiated Services Networks.

The approach taken by DiffServ is to classify individual micro flows at the edge routers in the network, into one of the many classes and then apply a per-class service in the core of the network. It employs various Active Queue Management techniques for queue management which are basically divided into two approaches. One is backlog based (BB) control, like tail-drop, RED, WRED (weighted red), measure only the number of packets in the buffer to determine the severity of congestion. And other is arrival rate based (RB) control schemes, such as REM or GREEN, measure the packet arrival rate, as well as possibly the backlog, to estimate congestion.


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