1. USING NEURAL NETWORKS IN ACTIVE QUEUE MANAGEMENT MARCIN WASOWSKI

2. QUEUE MANAGEMENT

3. QUEUE MANAGEMENT PASIVE 1) drop-head: when a packet arrives to a full queue, drop the first packet in line 2) random drop: when a packet arrives to a full queue, drop random packet (more complex) ACTIVE It is a technique that consists in dropping packets before a router's queue is full

4. ACTIVE QUEUE MANAGEMENT Active approach: early dropping when congestion arises – give sources enough time to react to congestion before queues fill up – do not keep queues full – drop packets selectively to avoid global synchronization

5. RED is an Active Queue Management scheme for Internet routers – tailored for TCP connections across IP routers RED design goals – congestion avoidance – global synchronization avoidance – avoidance of bias against bursty traffic – bound on average queue length to limit delay RED – RandomEarlyDetection

6. RED – Random Early Detection avg(t)=(1-w)*avg(t-1) + w*q(t) p(t)= Maxdrop *(avg(t)-Minth)/(Maxth-Minth)

7. ARED Minth and Maxth are changing the packet drop rate increases linearly from zero, when the average queue size is at the RED parameter minth, to a drop rate of when the average queue size reaches maxth.

8. WRED Weight Random Early Detection

9. USING NEURAL NETWORKS

10. STEPS 1) SIMULATION NETWORK WORKING IN NS 2 (NETWORK SIMULATOR 2) SOFTWARE 2) LEARNING NEURAL NETWORKS

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