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# Using neural networks in active queue manegment

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### Using neural networks in active queue manegment

1. 1. USING NEURAL NETWORKS IN ACTIVE QUEUE MANAGEMENT<br />MARCIN WASOWSKI<br />
2. 2. QUEUE MANAGEMENT<br />
3. 3. QUEUE MANAGEMENT<br />PASIVE <br />1) drop-head: when a packet arrives to a full queue, drop the first packet in line<br />2) random drop: when a packet arrives to a full queue, drop random packet (more complex)<br />ACTIVE <br />It is a technique that consists in dropping packets before a router's queue is full<br />
4. 4. ACTIVE QUEUE MANAGEMENT<br />Active approach: early dropping when congestion arises<br />– give sources enough time to react to congestion before queues fill up<br />– do not keep queues full<br />– drop packets selectively to avoid global synchronization<br />
5. 5. RED is an Active Queue Management scheme for Internet routers<br />– tailored for TCP connections across IP routers<br />RED design goals<br />– congestion avoidance<br />– global synchronization avoidance<br />– avoidance of bias against bursty traffic<br />– bound on average queue length to limit delay<br />RED – RandomEarlyDetection<br />
6. 6. RED – Random Early Detection<br />avg(t)=(1-w)*avg(t-1) + w*q(t)<br />p(t)= Maxdrop *(avg(t)-Minth)/(Maxth-Minth)<br />
7. 7. ARED<br />Minth and Maxth are changing<br />the packet drop rate increases linearly from<br /> zero, when the average queue size is at the RED parameter minth, to a drop rate of when the average queue size reaches maxth.<br />
8. 8. WRED<br />Weight Random Early Detection<br />
9. 9. USING NEURAL NETWORKS<br />
10. 10. STEPS<br />1) SIMULATION NETWORK WORKING IN NS 2 (NETWORK SIMULATOR 2) SOFTWARE<br />2) LEARNING NEURAL NETWORKS<br />
11. 11. END<br />