white – achieving fair bandwidth allocation with priority dropping based on round trip time name :...
TRANSCRIPT
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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
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Outline
Introduction Related Work
Approach Evaluation Conclusion
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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
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Related Work
Random Early Detection (RED) Flow RED (FRED) Core-Stateless Fair Queuing (CSFQ) Deficit Round-Robin (DRR)
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RED [FJ93]
Based on average queue size
minth maxth queuesize
0
1
max_p
MinthMaxth
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FRED [LM97]
Modification to RED Maintains per-flow state information
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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
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DRR
Implementation of Fair Queuing Maintains per-flow state information
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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
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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
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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
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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]
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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
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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
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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
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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
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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.
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minth maxth queuesize
0
1
max_p
WHITE Algorithm
qave
robust flowfragile flow
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Outline
Introduction Approach Evaluation
Setup Experiments Chardonnay vs. Chablis
Conclusion
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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
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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
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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)
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Uniformly Distributed Latencies
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Uniformly Distributed Latencies
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Uniformly Distributed Latencies
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Uniformly Distributed Latencies
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Uniformly Distributed Latencies
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Uniformly Distributed Latencies
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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)
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Unbalanced Latencies:1 Robust vs. 29 Fragile
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Unbalanced Latencies:1 Robust vs. 29 Fragile
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Unbalanced Latencies:1 Robust vs. 29 Fragile
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Unbalanced Latencies:1 Robust vs. 29 Fragile
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Unbalanced Latencies:1 Robust vs. 29 Fragile
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Unbalanced Latencies:1 Robust vs. 29 Fragile
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Unbalanced Latencies:1 Fragile vs. 29 Robust
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Unbalanced Latencies:1 Fragile vs. 29 Robust
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Unbalanced Latencies:1 Fragile vs. 29 Robust
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Unbalanced Latencies:1 Fragile vs. 29 Robust
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Unbalanced Latencies:1 Fragile vs. 29 Robust
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Unbalanced Latencies:1 Fragile vs. 29 Robust
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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)
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Dynamic Latencies
Robust
Average
Fragile
0s 60s 90s 120s30s
A B C D
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Dynamic Latencies
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Dynamic Latencies
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Dynamic Latencies
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Dynamic Latencies
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Dynamic Latencies
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Dynamic Latencies
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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
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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
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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
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Outline
Introduction Approach Evaluation Conclusion
Future Work
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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
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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