The Impact of Active Queue Management on Multimedia Congestion Control

1. The Impact of Active Queue Management on Multimedia Congestion Control Wu-chi Feng Ohio State University

2. Multimedia Applications • Recent proliferation of streaming multimedia applications • VoIP, Real Audio, Real Video, etc. • Increasing packet loss rates due to non-adaptive applications • Potential for congestion collapse

3. TCP Congestion Control • Instrumental in preventing congestion collapse • Cooperative, window-based flow control done at every source • Multiplicative decrease of window on packet loss • Additive increase of window for congestion avoidance

4. RED Queue Management • IETF/IRTF recommendation to reduce packet loss in the Internet • Improves performance of TCP congestion control • Mechanisms for identifying and penalizing non-adaptive applications

5. RED Algorithm • Keep exponentially weighted moving average of queue length (EWMA) • If (EWMA < minth) { queue packet } • If (minth < EWMA < maxth) • drop packet with probability pdrop • p=maxp*(EWMA-minth)/(maxth-minth) • pdrop = p*f(count) • If (EWMA > maxth) {drop packet }

6. RED Extensions • Extensions added to penalize non-adaptive applications • RED with penalty box • Keep track of last n packet losses • Penalize flows with the largest number • FRED • Restrict buffer occupancy of flows to a fair share

7. Implications • Multimedia applications will need to implement TCP-compatible rate control or else • Problem: TCP + RED = Trouble for streaming multimedia applications

8. Why? • Congestion notification (CN) random • CN spread out randomly over large time scales • Each TCP connection experiences bandwidth jitter over short and long time scales • Detrimental to streaming multimedia

9. Experiment Run with 50KB RED queues so that buffer size of drop-tail and RED are effectively equal. Effective size of 20KB RED queue is less than 20KB due to early detection Effective size of 50KB RED queue is at least 20KB since minth=20KB • Idealized scenario simulated using ns (http://www-mash.cs.berkeley.edu/ns/) • 4 connections over small network • 20K drop-tail queues • 20K RED queues (minth=5K, maxth=15K) • 50K RED queues(minth=20K, maxth=40K) 10Mbs 10Mbs 45Mbs 10Mbs 10Mbs Source Destination

10. Drop-tail Queues (20KB) • Bandwidth over 1 and 8 sec. intervals

11. RED queues (20KB) • Bandwidth over 1 and 8 sec. intervals

12. RED queues (50KB) • Bandwidth over 1 and 8 sec. intervals

13. Impact on Multimedia Apps. • Increased jitter requires additional buffering at the destination to smooth out • Introduces additional delay in playback of stream • Detrimental to interactive applications such as video conferencing

14. Bandwidth Variation (4 sources)

15. Bandwidth Variation (10 sources) • RTOs introduce significant variation

16. Conclusions • Active queue management algorithms cause significant bandwidth variation to TCP-compatible sources • Additional buffering and playback delay necessary • Requires smoother rate/congestion control algorithms