# Fuzzy RED: Congestion control for TCP/IP Diff-Serv - PowerPoint PPT Presentation

Title: Fuzzy RED: Congestion control for TCP/IP Diff-Serv

1
Fuzzy RED Congestion control for TCP/IP Diff-Serv
• Explosion of Internet new congestion control
method is needed
• WHY?
• Users now demand for integrated services network
• New services with high bandwidth demands (video
on demand, video conferencing, etc)
• Need for quality of service demanded by new
applications (e.g. streaming video)
• Current internet infrastructure can only support
best effort traffic

2
Diff-Serv
• A more evolutionary approach than Int-Serv
• In Internet based applications they were in some
cases bigger than the existing connection period
• Diff-Serv does not require significant changes to
the Internet Infrastructure
• Uses existing ToS bits in IP header of service
differentiation
• Works in the edges of a network
• Provides QoS using drop-preference algorithm

3
Diff-Serv
• Differentiation of services is provided through 3
classes of services called per-hop behaviour
• Expedited Forwarding EF
• Low losses
• Very low queuing delays
• Allocation of resources through SLA at connection
setup

4
Diff-Serv
• Assured Forwarding AF
• Low packet losses
• Has 3-4 independent forward classes
• Each such class has 2-3 different drop
preferences
• Preferentially drops best-effort packets and
non-conforming packets when congestion occurs

5
RED
• Most popular algorithm used for Diff-Serv
networks
• for each packet arrival
• calculate the average queue size avg
• if minth ? avg ? maxth calculate probability
pawith probability pa mark the arriving packet
• else if maxth ? avg
• mark the arriving packet
• Explain what mark means

6
RED
• Uses min, max dropping thresholds for each class
• The algorithm used for calculating the queue
average determines the allowed degree of
burstiness
• The pa probability is a function of the average
queue size. Varies linearly from 0 to 1.

7
RED
8
Fuzzy RED
• We replaced fixed thresholds with an FLC
• FLC - Fuzzy Logic Controller
• Calculates pa based on two inputs, queue size and
queue rate of change
• The two inputs are described by fuzzy sets
• The FLC determines the pa by applying a set of
rules.
• Each class of service has an FLC

9
Fuzzy RED - The algorithm
• Algorithm
• for each packet arrival
• calculate queue size, queue rate of change
• calculate probability pa based on above metrics
• with probability pa mark the arriving packet
• pa is calculated by the FLC

10
Fuzzy RED Input Sets
• FLC set for queue - q (90 packet buffer)
• empty 0 0 18 35
• moderate 20 33 42 63
• full 44 64 90 90
• FLC set for queue rate of change - dq
• decreasing -44 -44 -7 1
• zero -14 0 0 14
• increasing 1 7 44 44

11
Fuzzy RED Output Set
• FLC set for pa
• zero 20 20 0 0
• low 0 0.10 0.10 0.20
• medium 0.15 0.20 0.20 0.30
• high 0.20 0.60 0.60 1.0
• As with input sets pa can also be different for
each class of service
• Best describe the behavior expected by the

12
Fuzzy RED - Rules
• Based on linguistic rules we calculate the
dropping probability
• Each class of service has its own definition of
set and rules
• Sample of rules
• If q is empty the pa is zero
• If q is full and dq is zero the pa is medium
• If q is full and dq is increasing then pa is high

13
Fuzzy RED Calculating pa
Input Evaluation
Output Evaluation
Rule If q is full and dq is decreasing
then pa is high
1
0.4
44 64 90 -20 -7 1
0.2
0.6 1
Input q is 68 and dq is -3 Output the weighted
average of the colored surface 0.4
14
Fuzzy RED - Conclusions
• Simplicity - just 3 steps
• Effectiveness
• Scalability - each class has its own FLC rule
file
• Robustness
• Currently
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## Fuzzy RED: Congestion control for TCP/IP Diff-Serv

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Title: Fuzzy RED: Congestion control for TCP/IP Diff-Serv

1
Fuzzy RED Congestion control for TCP/IP Diff-Serv
• Explosion of Internet new congestion control
method is needed
• WHY?
• Users now demand for integrated services network
• New services with high bandwidth demands (video
on demand, video conferencing, etc)
• Need for quality of service demanded by new
applications (e.g. streaming video)
• Current internet infrastructure can only support
best effort traffic

2
Diff-Serv
• A more evolutionary approach than Int-Serv
• In Internet based applications they were in some
cases bigger than the existing connection period
• Diff-Serv does not require significant changes to
the Internet Infrastructure
• Uses existing ToS bits in IP header of service
differentiation
• Works in the edges of a network
• Provides QoS using drop-preference algorithm

3
Diff-Serv
• Differentiation of services is provided through 3
classes of services called per-hop behaviour
• Expedited Forwarding EF
• Low losses
• Very low queuing delays
• Allocation of resources through SLA at connection
setup

4
Diff-Serv
• Assured Forwarding AF
• Low packet losses
• Has 3-4 independent forward classes
• Each such class has 2-3 different drop
preferences
• Preferentially drops best-effort packets and
non-conforming packets when congestion occurs

5
RED
• Most popular algorithm used for Diff-Serv
networks
• for each packet arrival
• calculate the average queue size avg
• if minth ? avg ? maxth calculate probability
pawith probability pa mark the arriving packet
• else if maxth ? avg
• mark the arriving packet
• Explain what mark means

6
RED
• Uses min, max dropping thresholds for each class
• The algorithm used for calculating the queue
average determines the allowed degree of
burstiness
• The pa probability is a function of the average
queue size. Varies linearly from 0 to 1.

7
RED
8
Fuzzy RED
• We replaced fixed thresholds with an FLC
• FLC - Fuzzy Logic Controller
• Calculates pa based on two inputs, queue size and
queue rate of change
• The two inputs are described by fuzzy sets
• The FLC determines the pa by applying a set of
rules.
• Each class of service has an FLC

9
Fuzzy RED - The algorithm
• Algorithm
• for each packet arrival
• calculate queue size, queue rate of change
• calculate probability pa based on above metrics
• with probability pa mark the arriving packet
• pa is calculated by the FLC

10
Fuzzy RED Input Sets
• FLC set for queue - q (90 packet buffer)
• empty 0 0 18 35
• moderate 20 33 42 63
• full 44 64 90 90
• FLC set for queue rate of change - dq
• decreasing -44 -44 -7 1
• zero -14 0 0 14
• increasing 1 7 44 44

11
Fuzzy RED Output Set
• FLC set for pa
• zero 20 20 0 0
• low 0 0.10 0.10 0.20
• medium 0.15 0.20 0.20 0.30
• high 0.20 0.60 0.60 1.0
• As with input sets pa can also be different for
each class of service
• Best describe the behavior expected by the

12
Fuzzy RED - Rules
• Based on linguistic rules we calculate the
dropping probability
• Each class of service has its own definition of
set and rules
• Sample of rules
• If q is empty the pa is zero
• If q is full and dq is zero the pa is medium
• If q is full and dq is increasing then pa is high

13
Fuzzy RED Calculating pa
Input Evaluation
Output Evaluation
Rule If q is full and dq is decreasing
then pa is high
1
0.4
44 64 90 -20 -7 1
0.2
0.6 1
Input q is 68 and dq is -3 Output the weighted
average of the colored surface 0.4
14
Fuzzy RED - Conclusions