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
  • Int-Serv had connection establish overheads.
  • 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
    network administrator

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
  • Advantages
  • Simplicity - just 3 steps
  • Effectiveness
  • Scalability - each class has its own FLC rule
    file
  • Robustness
  • Currently
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Title:

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

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... services with high bandwidth demands (video on demand, video conferencing, etc) Need for quality of service demanded by new applications (e.g. streaming video) ... – PowerPoint PPT presentation

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Transcript and Presenter's Notes

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
  • Int-Serv had connection establish overheads.
  • 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
    network administrator

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
  • Advantages
  • Simplicity - just 3 steps
  • Effectiveness
  • Scalability - each class has its own FLC rule
    file
  • Robustness
  • Currently
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