Transcript Title: An Adaptive Queue Management Method for Congestion Avoidance in TCP/IP Networks

Title: An Adaptive Queue Management
Method for Congestion Avoidance in
TCP/IP Networks
Presented By:
Frank Posluszny
Vishal Phirke
5/24/2016
Matt Hartling
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Outline
Background
Network Power
Simulation Topology
Weakness of RED - Motivation
Algorithm
Simulations & Comparisons RED Vs READ
READ Tuning
Conclusions and Future Work
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Background (1)
Goals:

Show drawbacks of RED with ECN

Propose new AQM: Random Early
Adaptive Detection
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Background (2)
TCP congestion control
 Congestion Control vs. Avoidance
 RED
 ECN

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Background (3)
ECN:
 Binary feedback scheme
 Router sets a bit in packet to “mark”
instead of drop
 ACK mirrors the marking back to
receiver
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Outline
Background
Network Power
Simulation Topology
Weakness of RED - Motivation
Algorithm
Simulations & Comparisons RED Vs READ
READ Tuning
Conclusions and Future Work
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What’s Power??
Throughput
Delay
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
Power =
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Response Time
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Outline
Background
Network Power
Simulation Topology
Weakness of RED - Motivation
Algorithm
Simulations & Comparisons RED Vs READ
READ Tuning
Conclusions and Future Work
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Simulation Topology
Bottleneck
Queue Size = 60 pkts
Pkt Size = 512 bytes
MINth= 15
MAXth= 45
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Outline
Background
Network Power
Simulation Topology
Weakness of RED - Motivation
Algorithm
Simulations & Comparisons RED Vs READ
READ Tuning
Conclusions and Future Work
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Weakness of RED - Motivation
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Weakness of RED - Motivation
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Weakness of RED - Motivation
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10 flows
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Weakness of RED - Motivation
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60 flows
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Weakness of RED - Motivation
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20 flows
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Outline
Background
Network Power
Simulation Topology
Weakness of RED - Motivation
Algorithm
Simulations & Comparisons RED Vs READ
READ Tuning
Conclusions and Future Work
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Random Early Adaptive Detection
Exponentially Weighted Moving Averages
Avgt+1 = (1-wq) avgt + wq qt
Old weighted average
Slt+1 = (1-wsl) slt
Old weighted slope
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Instantaneous queue
+ wsl (avgt+1 – avgt)
Instantaneous slope
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Random Early Adaptive Detection
At each change of MIN
(MAX + MIN)
level =
2
if(level > buffer * 0.52)
p = p + INC
INC = 0.02
if(level < buffer * 0.48)
p = p - DEC
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DEC = 0.002
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Outline
Background
Network Power
Simulation Topology
Weakness of RED - Motivation
Algorithm
Simulations & Comparisons RED Vs READ
READ Tuning
Conclusions and Future Work
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Fig 5: Throughput Vs. Delay
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READ Vs. RED (1)
RED:
 Lower Drop probability = Higher
Throughput & Higher Delay
 Higher Drop probability = Lower
Delay & Lower Throughput
READ:
 Always Lower Delay and Higher
Throughput
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Fig 6: Power (alpha=1)
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Fig 7: Power (alpha = 2)
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READ Vs. RED (2)
RED:
 Performance varies with maxp and
number of flows
 Performs worse than Drop Tail under
certain conditions
READ:
 Always performs better than RED
and Drop Tail
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Table 1: Throughput For Mixed Traffic
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Fig 8 & 9: Adaptation to Changes in Network
Conditions
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READ Vs. RED (3)
RED:
 Large variation in instantaneous and
average queue size
 Large variation in marking probability
 Marking probability varies with queue size
READ:
 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
Simulations & Comparisons RED Vs READ
READ Tuning
Conclusions and Future Work
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Fig 10: READ Tuning
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Outline
Background
Network Power
Simulation Topology
Weakness of RED - Motivation
Algorithm
Simulations & Comparisons RED Vs READ
READ Tuning
Conclusions and Future Work
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Conclusions 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
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