title: an adaptive queue management method for congestion avoidance in tcp/ip networks
DESCRIPTION
Title: An Adaptive Queue Management Method for Congestion Avoidance in TCP/IP Networks. Presented By:. Frank Posluszny. Vishal Phirke. Matt Hartling. Outline. Background. Network Power. Simulation Topology. Weakness of RED - Motivation. Algorithm. Simulations & Comparisons RED Vs READ. - PowerPoint PPT PresentationTRANSCRIPT
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Title: An Adaptive Queue Management Title: An Adaptive Queue Management Method for Congestion Avoidance in Method for Congestion Avoidance in TCP/IP NetworksTCP/IP Networks
Presented By:Presented By:
Frank Posluszny
Vishal Phirke
Matt Hartling
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Outline
Background
Network Power Simulation Topology
Weakness of RED - Motivation
Algorithm
Conclusions and Future Work
Simulations & Comparisons RED Vs READ
READ Tuning
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Background (1)Background (1)
Goals:
Show drawbacks of RED with ECN
Propose new AQM: Random Early Adaptive Detection
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Background (2)Background (2)
TCP congestion controlCongestion Control vs. AvoidanceREDECN
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ECN:Binary feedback schemeRouter sets a bit in packet to “mark”
instead of dropACK mirrors the marking back to
receiver
Background (3)Background (3)
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Outline
Background
Network Power Simulation Topology
Weakness of RED - Motivation
Algorithm
Conclusions and Future Work
Simulations & Comparisons RED Vs READ
READ Tuning
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What’s Power??
Throughput optimized N/W
-Great throughput- Takes 15minutes to view a web page.
Delay optimized N/W
-Low Delays – But the web page is missing a lot of information.….
Throughput Delay
Power = Throughput
Response Time
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Outline
Background
Network Power Simulation Topology
Weakness of RED - Motivation
Algorithm
Conclusions and Future Work
Simulations & Comparisons RED Vs READ
READ Tuning
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Simulation Topology
Bottleneck
Queue Size = 60 pktsPkt Size = 512 bytes
MINth= 15
MAXth= 45
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Outline
Background
Network Power Simulation Topology
Weakness of RED - Motivation
Algorithm
Conclusions and Future Work
Simulations & Comparisons RED Vs READ
READ Tuning
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Weakness of RED - Motivation
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Weakness of RED - Motivation
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Weakness of RED - Motivation 10 flows
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Weakness of RED - Motivation 60 flows
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Weakness of RED - Motivation 20 flows
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Outline
Background
Network Power Simulation Topology
Weakness of RED - Motivation
Algorithm
Conclusions and Future Work
Simulations & Comparisons RED Vs READ
READ Tuning
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Random Early Adaptive Detection
Exponentially Weighted Moving Averages
Avgt+1 = (1-wq) avgt + wq qt
Slt+1 = (1-wsl) slt + wsl (avgt+1 – avgt)
Instantaneous queueOld weighted average
Instantaneous slopeOld weighted slope
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Random Early Adaptive Detection
At each change of MIN
(MAX + MIN)
2 level =
if(level > buffer * 0.52)
p = p + INC
if(level < buffer * 0.48)
p = p - DEC
INC = 0.02
DEC = 0.002
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Outline
Background
Network Power Simulation Topology
Weakness of RED - Motivation
Algorithm
Conclusions and Future Work
Simulations & Comparisons RED Vs READ
READ Tuning
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Fig 5: Throughput Vs. DelayFig 5: Throughput Vs. Delay
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READ Vs. RED (1)READ Vs. RED (1)
RED:Lower Drop probability = Higher
Throughput & Higher DelayHigher Drop probability = Lower
Delay & Lower ThroughputREAD:Always Lower Delay and Higher
Throughput
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Fig 6: Power (alpha=1)Fig 6: Power (alpha=1)
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Fig 7: Power (alpha = 2)Fig 7: Power (alpha = 2)
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READ Vs. RED (2)READ Vs. RED (2)
RED:Performance varies with maxp and
number of flowsPerforms worse than Drop Tail under
certain conditionsREAD:Always performs better than RED
and Drop Tail
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Table 1: Throughput For Mixed TrafficTable 1: Throughput For Mixed Traffic
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Fig 8 & 9: Adaptation to Changes in Network Fig 8 & 9: Adaptation to Changes in Network ConditionsConditions
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READ Vs. RED (3)READ Vs. RED (3)
RED: Large variation in instantaneous and
average queue size Large variation in marking probability Marking probability varies with queue sizeREAD: Less variation in marking probability and
queue size Large, periodic fluctuations
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Outline
Background
Network Power Simulation Topology
Weakness of RED - Motivation
Algorithm
Conclusions and Future Work
Simulations & Comparisons RED Vs READ
READ Tuning
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Fig 10: READ TuningFig 10: READ Tuning
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Outline
Background
Network Power Simulation Topology
Weakness of RED - Motivation
Algorithm
Conclusions and Future Work
Simulations & Comparisons RED Vs READ
READ Tuning
![Page 31: Title: An Adaptive Queue Management Method for Congestion Avoidance in TCP/IP Networks](https://reader036.vdocuments.site/reader036/viewer/2022062309/56813578550346895d9cdb78/html5/thumbnails/31.jpg)
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Conclusions and Future WorkConclusions and Future Work
Conclusions: RED can fail & too aggressive READ – reliable CA; higher power levels
Current & Future Work: Examine different increase/decrease
algorithms READ with different Network Topologies