1. SRED: Stabilized RED T. Ott, T.V. Lakshman, L. Wong Presented by King-Shan Lui

2. Diff. With RED SRED estimates number of active flows Misbehaving flows can be identified without keeping per-flow state Drop probabilities are adjusted according to number of active flows No computation of average queue length Assume TCP flows

3. Main Idea Number of active flows  number of different flows in the buffer A misbehaving flow has a lot of packets in the buffer When a packet arrives, compare it with a packet arrived before. If they belong to the same flow, a hit occurs.

4. Zombie List A list of M recently seen flows, zombies Longer memory than the buffer alone Information for each zombie: –Count: number of packets of this zombie received – timestamp: arrival time of the most recently received packet

5. Zombie List Operations Zombie list is not full –Insert the flow with count = 0, timestamp = t a Zombie list is full –Randomly pick a zombie Hit: count += 1, timestamp = t a No hit: with prob. p that the zombie is replaced The arrived packet may be dropped no matter there was a hit or not

6. Hits & Number of Active Flows Zombie list loses memory once every M/p packets Few active flows  more hits Misbehaving flows cause more hits than well-behaved flows

7. Hit Frequency P(t) – hit frequency around the time of the t th packet arrives at the buffer Hit(t) = 1 when hit; Hit(t) = 0, otherwise P(t) = (1 –  )P(t – 1) +  *Hit(t) Proposition: P(t) -1 is a good estimate for the effective number of active flows

8. Proposition Argument P(arrival packet belongs to flow i) =  i P(Hit(t)=1) =   i 2 1/N    i 2  1 Symmetric case: N flows,  i = 1/N –P(t) = 1/N (exact estimate) Asymmetric case: infinite flows,  i = 2 -i –P(t) = 3/16 (effective number of active flows)

9. Simple Stabilized RED Target buffer occupation – Q 0Target buffer occupation – Q 0 Set a drop probability – pSet a drop probability – p Square root law: congestion window of each flow, cwnd  p -1/2 Sum of N congestion windows – N * p -1/2 Q 0 = N*p -1/2  p = (N/Q 0 ) 2 p is proportional to N 2

10. Buffer capacity – B Current buffer size – q p zap = p sred (q) * p sred (q) = p max if 1/3*B  q < B = ¼ * p max if 1/6*B  q < 1/3*B = 0if 0  q < 1/6*B Simple Stablized RED (cont.)

11. p sred (q) Depends on current q, not history of q Three levels p sre d q p max B 1 Ratio 4: halving the congestion windows

12. p zap When number of flows  256 p zap ~ p sred /65356 * (number of flows) 2 When number of flows > 256 p zap = p sred Avoid p zap becomes too large p zap depends on q and P(t)

13. Full SRED Increase the drop probabilities of misbehaving flows

14. Simulation Results

15. SRED stablilizes the buffer occupancy when N  256 as q is independent of N q increases slightly when N = 1000 Buffer occupation almost never decreases below B/6 –could narrow the band where p sred (q) = p max /4

16. Contributions SRED provides a mechanism to estimate number of active flows and identify misbehaving flows SRED controls buffer occupancy by adjusting drop probabilities using estimated number of active flows

17. Remaining Issues Many parameters to be tuned: p sred, p max, , M, p, magic 256. Extra storage: Zombie list vs. per-flow state –M ~ 1000 > 256