1. Blue: An Alternative Approach to Active Queue Management
Wu-chang Feng
June 23, 2001

2. Outline
Motivation
Congestion control and queue management today (TCP, Drop-tail, RED)
Blue
Conclusion

3. Motivation Exponential increase in network demand Goal
Rise in packet loss rates
17% loss rates reported [Paxson97]
Decrease in link utilization and goodput
Potential for congestion collapse [Jacobson88]
Goal
Queue management algorithms for maximizing network efficiency in times of heavy congestion
0% packet loss, 100% link utilization, low queuing delay

4. Congestion control today
TCP
Instrumental in preventing congestion collapse
Limits transmission rate at the source
Window-based rate control
Increased and decreased based on implicit signals from the network (acknowledgments and packet loss)
Slow-start
Fast-retransmit, Fast-recovery
Congestion avoidance

5. Drop-tail queue management
Default queue management mechanism
Packets dropped upon queue overflow
Problems
Global synchrony (poor utilization)
Late congestion notification (packet loss)
Solution
Randomize
Early detection of incipient congestion

6. RED queue management RED (Random Early Detection) [Floyd93]
Keep EWMA of queue length (Qave) using weight wq
Increase in EWMA triggers random drops
Basic algorithm 1
Pdrop
maxp
minth
maxth
Qave

7. Problems with RED Hard to tune Does not adapt well to traffic
[Feng99], [Anjum99], [Christiansen2000], [May99], [Ott99], ….
Why?
Wrong control variable being used
Queue length indicates congestion not severity

8. Blue Class of fundamentally different queue management algorithms
Decouple congestion management from queue length
Rely only on queue and link history
Example
Increase aggressiveness when loss rates high
Decrease aggressiveness when link underutilized

9. RED example Sending rate increases above L Mbs
Queue increases some more
Rate > L Mbs
Sinks generate DupACKs/ECN
DupACKs/ECN travel back
Queue increases some more
Rate > L Mbs
L Mbs
Sources A B
Sinks
Sources detect congestion
Queue overflows, maxth triggered
Queue clears, but under-utilization imminent
Sources see sustained CN
Rate < L Mbs
Rate > L Mbs
Queue increases
Queue increases, EWMA increases to trigger RED
Rate > L Mbs

10. Ideal example (Blue) L Mbs Sources A B Sinks Rate = L Mbs
Sinks generate
DupACKs/ECN
Queue drops and/or ECN marks at steady rate
Rate = Exactly what will keep sources at L Mbs

11. Example Blue algorithm
Single dropping/marking probability
Increase upon packet loss
Decrease when link underutilized
Freeze value upon changing
Upon packet loss:
if ((now - last_update) > freeze_time) then
Pmark = Pmark + d1
last_update = now
Upon link idle:
Pmark = Pmark - d2

12. Blue parameter selection
freeze_time: set to prevent multiple updates on a single round-trip (~10ms-100ms)
d1/d2: set to allow pm to range from 0 to 1 on the order of seconds
[Paxson97], [Balakrishnan98]

13. Evaluation 1000 and 4000 Pareto on/off TCP sources
Router queue sizes 100KB (17.8ms) to 1000KB (178ms)
RED (wq = , 0.002, 0.02, 0.2)
Blue
freeze_time = 10ms, 100ms
d1 = 0.02, ; d2 = 0.002,
100 Mbs
100 Mbs
45 Mbs
45 Mbs
10ms
10ms
1ms, 5ms, 20ms
1ms, 5ms, 20ms

14. Blue evaluation
1000 sources

15. Blue evaluation
4000 sources

16. Understanding Blue Experiment Queue length plots
50 sources added every 10 seconds
Queue length plots
Blue
RED

17. Understanding Blue
Marking behavior
RED
Blue

18. Understanding Blue w/ECN timeouts
Lack of ECN timeouts allows packet loss with pm=1
With ECN timeouts Blue similarly outperforms RED
RED
Blue

19. Understanding Blue w/ECN timeouts
Marking behavior
RED
Blue

20. Blue evaluation w/ECN timeouts
1000 sources

21. Blue evaluation w/ECN timeouts
4000 sources

22. Implementation FreeBSD 2.2.7 + ALTQ Winbook XL (233 MHz/32 MB)
IBM PC 365
(200 MHz/64 MB)
100 Mbs
100 Mbs
10 Mbs
Intellistation Mpro
(400 MHz/128 MB)
Intellistation Zpro
(200 MHz/64 MB)
Thinkpad 770
(266 MHz/64 MB)
IBM PC 360
(150 MHz/64 MB)

23. Blue Evaluation
Loss rates
Link utilization

24. Conclusion