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 ………
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