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

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26 Οκτ 2013 (πριν από 3 χρόνια και 7 μήνες)

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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

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

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

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

RED

Most popular algorithm used for Diff
-
Serv networks:



for each packet arrival


calculate the average
queue size avg

if min
th



avg



max
th


calculate probability
p
a

with probability
p
a
: mark the arriving packet

else if
max
th




avg


mark the arriving packet

Explain what mark means


RED


Uses min, max dropping thresholds for each
class


The algorithm used for calculating the
queue average determines the allowed
degree of burstiness


The p
a

probability is a function of the
average queue size. Varies linearly from 0
to 1.

RED

EF

AF Class

Best Effort

Check and

traffic

shaping

Priority Queue

Yes

No

Discard

RIO Queue

Min

Max

Fuzzy RED


We replaced fixed thresholds with an FLC


FLC
-

Fuzzy Logic Controller:


Calculates p
a

based on two inputs, queue size
and queue rate of change


The two inputs are described by fuzzy sets


The FLC determines the p
a

by applying a
set of rules.


Each class of service has an FLC


Fuzzy RED
-

The algorithm

Algorithm:

for each packet arrival


calculate queue size, queue rate of change

calculate probability p
a
based on above metrics

with probability p
a

mark the arriving packet


p
a

is calculated by the FLC


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

Fuzzy RED


Output Set


FLC set for p
a


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 p
a

can also be different for each
class of service


Best describe the behavior expected by the
network administrator

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 p
a

is zero


If q is full and dq is zero the p
a

is medium


If q is full and dq is increasing then p
a

is high

Fuzzy RED


Calculating p
a

Input Evaluation

Output Evaluation

Rule: If q is full and dq is decreasing then

pa is high

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

1

0.4

Fuzzy RED
-

Conclusions

Advantages:


Simplicity
-

just 3 steps


Effectiveness


Scalability
-

each class has its own FLC rule
file


Robustness


Currently ………