crease of the average queue size, whereas transient bursts in buffer have only little influence. Following describes the RED algorithm in detail. Let qn denote the current queue length, i.e. the number of packets in the system (including the one in service) at the time of nth packet arrival. Let Kn denote the average queue length at the time of nth arrival. The nth packet will be discarded with probability Pn that is determined RED drop function, which is a linear function that is determined by the following parameters: maxp: maximum dropping probability. minth: minimum average queue length threshold for packet dropping. maxth: maximum average queue length threshold. The average queue length Kn at the time of nth arrival. For each arriving packet we compute the average (smoothened) queue length. The average queue size is based on the previous average Kn-1 (the old average queue length at the time of (n-1)th arrival) and the current size of the queue qn. The formula is given following: (A-1) Here W is the exponential weight factor, a user-configurable value. For high values of W, the previous average becomes more important. A large factor smoothes out the peaks and lows in queue length. The average queue size is unlikely to change very quickly, avoiding drastic swings in size. The RED process will be slow to start dropping packets, but it may continue dropping packets for a time after the actual queue size has fallen below the minimum threshold. The slow-moving average will accommodate temporary bursts in traffic. But if value of W gets too high, RED will not react to congestion. Packets will be transmitted dropped as if RED were not in effect. For low values of W, the average queue size closely tracks the current queue size. The resulting average may fluctuate with changes in the traffic levels. In this case, the RED process responds quickly to long queues. Once the queue falls below the minimum threshold, the process will stop dropping packets. If the value of W gets too low, RED will overreact to temporary traf