1. Max Min Fairness How define fairness?
“Any session is entitled to as much network use as is any other”
….unless some sessions can use more without hurting others
Other definitions
Network usage depends on the resource consumption by the session
Pay/bid for what you use

2. A Simple Example
Max-min allocation: 1/3, 1/3, 1/3, 2/3

3. How to Calculate Max Min Flow Share
Fluid model:
Increase the flow until some pipe fills-up.
Fix the bandwidth of the bottleneck flows
Continue with the unfixed flows
Can be done efficiently by calculating the bottleneck link at step 1

4. Buffer management and admission control
Simplest admission policy:
Accept packets until buffer is full (tail drop)
We already saw:
Tail drop is not kind to TCP flows
RED can be used to avoid tail drop

5. Reminder: Hallelujah for RED
Random early detection (RED) makes three improvements
Metric is moving average of queue lengths
small bursts pass through unharmed
only affects sustained overloads
Packet drop probability is a function of mean queue length
prevents severe reaction to mild overload
Can mark packets instead of dropping them
allows sources to detect network state without losses
RED improves performance of a network of cooperating TCP sources
No bias against bursty sources
Controls queue length regardless of endpoint cooperation
ECN

6. How does it work? gentle RED RED Average Q size Drop probability
Max-thresh
2*Max-thresh
max-p 1

7. So problem is solved? Fairly easy to implement in hardware!
Can work in wire-speed!
All we need to do is set the parameters…..right 
Turns out there is no universal good set of parameters
Some studies show RED has NO advantage over tail drop. WHY?

8. parameters avgQ= (1-wq)avgQ+wqq Floyd-Jacobson:
Wq: 0.002, not less than 0.001
max_p: 1/50,
max_th: at least twice min_th
max_th-min_th larger than the q increase in RTT
Future work …..

9. So does it help us to surf?
Tuning RED for Web Traffic, Christiansen et al., SIGCOMM 2000
compared to a (properly configured) FIFO queue, RED has a minimal effect on HTTP response times for offered loads up to 90% of link capacity,
response times at loads in this range are not substantially effected by RED control parameters,
between 90% and 100% load, RED can be carefully tuned to yield performance somewhat superior to FIFO, however, response times are quite sensitive to the actual RED parameter values selected, and
in such congested networks, RED parameters that provide the best link utilization produce poorer response times.

10. SPRINT study (Diot et al.)
A parallel study, presented at NANOG 2000
Testbed
with CISCO routers (7500)
with Dummynet
used “recommended” RED and GRED parameters
Heterogeneous delays (120 to 180 ms)

11. Traffic characteristics
16 to 256 TCP connections sharing the bottleneck.
Experimental traffic generated by Chariot
long-lived TCP connections.
more “realistic” traffic mix:
90% short lived TCP connections (up to 20 packets)
10 % long lived TCP connections
1Mbps UDP in both cases

12. Testbed (CISCO routers)
7500
7500
10 Megs

13. Testbed (Dummynet)
7500
7500
10 Megs
Dummy
net
100 Megs

14. What is Dummynet?
application
dummynet
network

15. Metrics observed Aggregate goodput through a router
TCP and UDP loss rate
Consecutive losses
Queuing behavior

16. Aggregate goodput (long-lived TCP)

17. 256 short and long lived TCP connections

18. Consecutive packet losses (long lived)

19. …if we use “optimal” RED parameters

20. Consecutive packet losses (realistic traffic mix)

21. Queuing behavior (256 long lived connections)

22. Queuing behavior (256 connections, realistic mix)

23. Diot’s summary
No significant difference on goodput, TCP losses and UDP losses.
On consecutive losses, clear advantage to GRED and GRED-I.
“gentle” modification solves many RED problems.
Oscillations: no clear winner. Traffic seems to be the determining factor.

24. From the ISP standpoint ...
Not clear there is an advantage in deploying RED, GRED, or GRED-I.
Maybe GRED-I is an option if one can find a “universal” exponential dropping function.
ECN will work with any scheme.
Not clear the solution is in the AQM space.

25. GRED-I with exponential dropping function1
buffer size

26. About Fair Queuing ... Not only feasible … easy at the edges!
(an example)
vendors support from 64k to 200k flows
Really fair
everybody gets what he/she paid for
local signaling (end host to CPE)

27. fair queueing at the edge
Core-stateless fair queueing
WFQ is hard to do at the core
Edge routers estimate rate and label packets
Core routers maintain FIFO queues and drop based on label

29. CSFQ summary Better than FIFO and RED Similar to FRED
Not as good as DRR

30. Rainbow fair queueing Similar to CSFQ Have similar performance as CSFQ
Enable applications to mark packets and achieve better goodput

31. Rainbow Fair Queueing (RFQ)
Example
A: 10 Kbps B: 6 Kbps C: 8 Kbps
Each layer: 2 Kbps

32. RFQ: basic mechanism
(1) the estimation of the flow arrival rate at the edge routers
(2) the selection of the rates for each color
(3) the assignment of colors to packets
(4) the core router algorithm

33. Rainbow Fair Queueing (RFQ)
(1) the estimation of the flow arrival rate at the edge routers
rinew: arrival rate
tik: arrival time of flow I
lik: length of the kth packet of flow I
K: a constant
Tik = tik – tik-1

34. Rainbow Fair Queueing (RFQ)
(2) the selection of the rates for the rates for each color
ci: i color average rate of packets
N: total number of colors and multiple of b
a,b: determine the block structure
P: the maximum flow rate in the network

35. Rainbow Fair Queueing (RFQ): Example
N=8 a=b=2
c c c c c c c c7
P/16 P/16 P/16 P/16 P/8 P/ P/4 P/4

36. Rainbow Fair Queueing (RFQ)
(3) the assignment of colors to packets
Suppose the current estimate of the flow arrival rate is r, and j is the smallest value satisfying
Then the current packet is assigned color with probability

37. Conditions to decrease color:
(4) the core router algo.
Conditions to decrease color:
q threshold
Flow bw
Positive gradient
Hold you horses
Conditions to increase color
Time
Flow below service rate

38. Rainbow Fair Queueing (RFQ)
Weighted RFQ
wi: weight for flow i
cj = wicj

39. Simulations: A single congested link

40. Fairness: flow i sends at 0.313i

41. Throughput: TCP flow

42. Throughput: UDP flows

43. Control Responsiveness 10Mbps: 8x1M7x1M+8M

44. Simulations: Performance Effects of Buffer Size

45. Simulations: TCP Performance Under Various round Trip Delay