Wireless Ad Hoc and Sensor Networks

Wireless Ad Hoc and Sensor Networks Wireless Ad Hoc and Sensor Networks

12.07.2015 Views

Congestion Control in ATM Networks and the Internet 97Example 3.3.1: Multiple ON/OFF SourcesTo evaluate the performance of the control schemes in the presence ofON/OFF traffic, network congestion was created by altering the servicecapacity at the destination switch as follows:S r = 48,000 cells/sec, 0 ≤t≤360msec,= 45,000 cells/sec,361 ≤t≤720msec,= 40,000 cells/sec, 721 ≤t≤1080msec,(3.32)= 45,000 cells/sec,1081 ≤t≤1440msec,= 48,000 cells/sec,1441 ≤t≤1800msec.Congestion was created by reducing the service capacity to 40,000 cells/sec (less than the MCR). Figure 3.6 and Figure 3.7 (same as Figure 3.6, butwith threshold scheme removed) show the CLR with time when threshold,adaptive, one-layer, two-layer — with and without offline training-basedCell loss ratio0.0450.040.0350.030.0250.020.015ThresholdAdaptiveOne layerTwo layer w/oTwo layer withBuffer length = 2500.010.0050200 400 600 800 1000 1200 1400 1600 1800Time in msecFIGURE 3.6Cell loss ratio with congestion.

98 Wireless Ad Hoc and Sensor NetworksCell loss ratio1.6 × 10−31.41.210.80.60.4AdaptiveOne layerTwo layer w/o trainingTwo layer with trainingBuffer length = 2500.20200 400 600 800 1000 1200 1400 1600 1800Time in msecFIGURE 3.7Cell loss ratio with congestion.congestion control schemes — were deployed at the network switch withbuffer length of 250 cells. The service capacity at the switch for differentintervals of time is given by Equation 3.32. As expected, the CLR for thethreshold, ARMAX, the one-layer NN method increases as the servicecapacity is decreased, reaches a maximum value when the service capacityis reduced to a small value during the time interval of 721 ≤t≤1080msec.The CLR again decreases as the service capacity is increased toward thePCR of the combined traffic.In contrast, the CLR for the two-layer NN method, with and without apriori training, remains near zero throughout the simulation time, evenwhen the service capacity was reduced to 20,000 cells/sec, which impliesthat the two-layer NN controller performs better than all other methodsduring congestion in controlling the arrival rate of the cells into the destinationbuffer. As all the traffic used was of ON/OFF type, the sourcerates were reduced fairly and quickly using the proposed scheme, resultingin a low CLR. Further, a transmission delay of approximately 25 msec(

98 <strong>Wireless</strong> <strong>Ad</strong> <strong>Hoc</strong> <strong>and</strong> <strong>Sensor</strong> <strong>Networks</strong>Cell loss ratio1.6 × 10−31.41.210.80.60.4<strong>Ad</strong>aptiveOne layerTwo layer w/o trainingTwo layer with trainingBuffer length = 2500.20200 400 600 800 1000 1200 1400 1600 1800Time in msecFIGURE 3.7Cell loss ratio with congestion.congestion control schemes — were deployed at the network switch withbuffer length of 250 cells. The service capacity at the switch for differentintervals of time is given by Equation 3.32. As expected, the CLR for thethreshold, ARMAX, the one-layer NN method increases as the servicecapacity is decreased, reaches a maximum value when the service capacityis reduced to a small value during the time interval of 721 ≤t≤1080msec.The CLR again decreases as the service capacity is increased toward thePCR of the combined traffic.In contrast, the CLR for the two-layer NN method, with <strong>and</strong> without apriori training, remains near zero throughout the simulation time, evenwhen the service capacity was reduced to 20,000 cells/sec, which impliesthat the two-layer NN controller performs better than all other methodsduring congestion in controlling the arrival rate of the cells into the destinationbuffer. As all the traffic used was of ON/OFF type, the sourcerates were reduced fairly <strong>and</strong> quickly using the proposed scheme, resultingin a low CLR. Further, a transmission delay of approximately 25 msec(

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