WHITE – Achieving Fair Bandwidth Allocation with Priority Dropping Based on Round Trip Time

Name : Choong-Soo LeeAdvisors : Mark Claypool, Robert Kinicki

Reader : Craig WillsDate: March 25, 2002

Outline

Introduction Related Work

Approach Evaluation Conclusion

Introduction

Current internet uses routers with droptail queue management Droptail introduces the problem of global synch

ronization There are many active queue managements

proposed but most of them are concerned with overall throughput and delay but not with fairness Flows are not homogeneous but heterogeneous

Robust flows vs. Fragile flows

Related Work

Random Early Detection (RED) Flow RED (FRED) Core-Stateless Fair Queuing (CSFQ) Deficit Round-Robin (DRR)

RED [FJ93]

Based on average queue size

minth maxth queuesize

0

1

max_p

MinthMaxth

FRED [LM97]

Modification to RED Maintains per-flow state information

CSFQ [SSZ98]

Rate-based Active Queue Management Distinguishes between edge and core

routers Edge routers label packets Core routers use these labels to treat packets

fairly Estimates fair share and uses it to drop

packets

DRR

Implementation of Fair Queuing Maintains per-flow state information

Overview

Goals Achieve fair allocation close to Fair Queuing and comparable or

better than RED, FRED and CSFQ under most scenarios. Reduce complexity by not having to maintain per flow state

Per Packet

No Per Packet

Per Flow No Per Flow

DRR FRED WHITECSFQ

RED

Outline

Introduction Approach

Round Trip Time at the Edge Average Round Trip Time at the Router Drop Probability Based on Round Trip Times

Evaluation Conclusion

Approach Modification to RED

Adjusts max_p per packet Supports both dropping and marking of packets

Dropping vs. Marking Dropping WHITE : Chardonnay Marking WHITE : Chablis

Round Trip Time at the Edge Average Round Trip Time at the Router Drop Probability Based on Round Trip Times

Round Trip Time at the Edge

Edge Hint Packets get labeled with additional information We want the lowest RTT as our hint

Modification to TCP-Reno with TCP-Vegas RTT Computation

4-17 bits in the IP header available for additional information if no fragmentation [SZ99]

Average Round Trip Time at the Router

Now that we have the RTT edge hint, RTTs are exponentially weighted (Raverage) at the router

Due to high fluctuation of Raverage, we use extra steps to compute stabilized value of RTT (Rformula) How long it has been out of 12.5ms

average RTT average RTTR 1 w R w p.RTT

Drop Probability Based on Round Trip Time

Now, we want to use RTT edge hint and average RTT at the router to compute drop probability

TCP-Friendly Formula [PFK98]

Simplify

2RTO

sT

2p 3pR t p 1 32p

3 8

a

sT

cRp T1 = T2

Drop Probability Based on Round Trip Time

1 2

a a1 1 2 2

a a 12 1

2

1

a1

2 12

T T

s s

cR p cR p

Rp p

R

Rp p

R

formularobust base

robust

formulafragile base

fragile

Rp p

R

Rp p

R

Drop Probability Based on Round Trip Time

0

0.2

0.4

0.6

0.8

1

1.2

0 0.1 0.2 0.3 0.4 0.5

Drop Probability

sqrt(p) sqrt(p) 3̂ sqrt(p) 7̂ Sum Power (Sum)

0

0.5

1

1.5

2

2.5

3

3.5

0.5 0.6 0.7 0.8 0.9 1

Drop Probability

sqrt(p) sqrt(p) 3̂ sqrt(p) 7̂ Sum Power (Sum)

3 71.58p p p c p 3 7

0.71p p p c p

Drop Probability Based on Round Trip Time

For Chardonnay, 0.71 corresponds to robust) and 1.58 to fragile).

For Chablis, 1.58 corresponds to both robust) and fragile).

However, simulation results showed that values of (0.65, 1.4) worked the best for Chardonnay and (1.6, 1.4) for Chablis.

minth maxth queuesize

0

1

max_p

WHITE Algorithm

qave

robust flowfragile flow

Outline

Introduction Approach Evaluation

Setup Experiments Chardonnay vs. Chablis

Conclusion

Setup

Network Simulator 2 (NS-2) was used to run all the simulations.

Modification to source code to include RTT edge hints and to implement WHITE.

We ran 6 experiments with RED, FRED, CSFQ, DRR, Chardonnay and Chablis

Setup

N0

N1

N2

N29

R D

Queue Size: 12010 Mbps, 5ms

5 Mbps

RED/FREDminth: 10maxth: 30wq: 0.0008max_p: 0.1

WHITE(Chardonnay, Chablis)minth: 10maxth: 30Wq: 0.0008max_p: 0.1: 0.65, 1.6: 1.4, 1.4

CSFQK: 100msK: 100msKc: 100ms

Experiments

Uniformly Distributed Latencies (Exp1) Round trip latencies from sources were 20ms,

30ms, 40ms, … , 310ms. Balanced Clustered Latencies (Exp2) Unbalanced Latencies (Exp3, Exp4) Dynamic Latencies (Exp5, Exp6)

Uniformly Distributed Latencies

Uniformly Distributed Latencies

Uniformly Distributed Latencies

Uniformly Distributed Latencies

Uniformly Distributed Latencies

Uniformly Distributed Latencies

Experiments

Uniformly Distributed Latencies (Exp1) Balanced Clustered Latencies (Exp2) Unbalanced Latencies

1 flow with 20ms round trip latency and 29 flows with 200ms round trip latency (Exp3)

1 flow with 200ms round trip latency and 29 flows with 20ms round trip latency (Exp4)

Dynamic Latencies (Exp5, Exp6)

Unbalanced Latencies:1 Robust vs. 29 Fragile

Unbalanced Latencies:1 Robust vs. 29 Fragile

Unbalanced Latencies:1 Robust vs. 29 Fragile

Unbalanced Latencies:1 Robust vs. 29 Fragile

Unbalanced Latencies:1 Robust vs. 29 Fragile

Unbalanced Latencies:1 Robust vs. 29 Fragile

Unbalanced Latencies:1 Fragile vs. 29 Robust

Unbalanced Latencies:1 Fragile vs. 29 Robust

Unbalanced Latencies:1 Fragile vs. 29 Robust

Unbalanced Latencies:1 Fragile vs. 29 Robust

Unbalanced Latencies:1 Fragile vs. 29 Robust

Unbalanced Latencies:1 Fragile vs. 29 Robust

Experiments

Uniformly Distributed Latencies (Exp1) Balanced Clustered Latencies (Exp2) Unbalanced Latencies (Exp3, Exp4) Dynamic Latencies

10 flows with 50ms round trip latency, 10 flows with 100ms round trip latency and 10 flows with 200ms round trip latency (Exp6)

Dynamic Latencies

Robust

Average

Fragile

0s 60s 90s 120s30s

A B C D

Dynamic Latencies

Dynamic Latencies

Dynamic Latencies

Dynamic Latencies

Dynamic Latencies

Dynamic Latencies

Overall Comparison

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1 3 4 6A 6B 6C 6DExperiment

Jain

's F

airn

ess

RED FRED CSFQ DRR Chardonnay Chablis

Chardonnay (Dropping) vs.Chablis (Marking)

0.820.84

0.860.88

0.90.92

0.940.96

0.981

1 2 3 4 5A 5B 5C 5D 6A 6B 6C 6D

Experiment

Jain

's F

airn

ess

Inde

x

Chardonnay Chablis

Chardonnay (Dropping) vs.Chablis (Marking)

Experiment Chardonnay Chablis

Drop (%) Goodput (Mbps)

Drop (%) Goodput (Mbps)

1 1.80 9.59 0.000 9.65

2 2.70 9.91 0.000 9.98

3 1.46 9.67 0.000 9.78

4 3.59 9.96 0.007 9.96

5 2.56 9.76 0.002 9.85

6 2.49 9.69 0.003 9.82

Outline

Introduction Approach Evaluation Conclusion

Future Work

Conclusion

Performance of Chardonnay and Chablis is better than RED, FRED and CSFQ and comparable to DRR RTT edge hints can be used to approximate DR

R’s performance without the complexity of maintaining per-flow state information

Marking performed better Less drops Better goodput

Future Work

Current version of WHITE does not support any non-responsive flows such as UDP flows

Adaptive mechanism is necessary to support much more flows than those in simulations