SEKE 2012 Proceedings - Knowledge Systems Institute

In addition to the rules, the knowledge base also
has membership functions used in fuzzification and
defuzzification processes of the values of input and output
respectively.
This paper proposes the use of Evolutionary Learning
and Fuzzy Logic to improve the performance of a load
balancer. This assistance is done through the dynamic
modification of the percentage of requests sent to each of
nodes served by the balancer.
With this objective, a module called COGNARE was
built. Its purpose is to perform a dynamic change in the rate
of requests sent to each of the nodes served by a balancer.
The inputs of this module are the current states of the
hardware such as memory, CPU, disk, network, etc.. The
output of this module is the allocation rate for each of the
resource managed by the load balancer.
A sensor agent software [3] was installed in each of
the nodes. Their function is to obtain the values of CPU
load, memory, disk, etc. and send them to COGNARE,
as illustrated in Figure 1. In this Figure, these agents are
represented by black circles.
Initially, the rules needed by fuzzy inference system
were learned. To that end, we sent requests to the balancer,
without using COGNARE. The workload percentages of
each node were changed in order to measure the processing
speed variation of the whole system. This change was made
randomly. After this, CPU and memory use of each node
was measured. Table 1 shows this.
CPU CPU Memory Memory Rate (%)
N 1 (%) N 2 (%) N 1 (%) N 2 (%) N 1 |N 2
64 21 17 19 79|21
53 47 13 22 63|37
7 89 11 52 11|89
87 13 18 13 81|19
Table 1: Table used in learning the rules. Just some of the
values obtained are showed.
The membership functions, illustrated in Figure 2, were
used to fuzzify values of the Table 1. In this Figure, VL, L,
M, H and VH mean very low, low, medium, high and very
high respectively. After this step, we generated Tables 2 and
3.
CPU CPU Memory Memory Rate N 1
N 1 N 2 N 1 N 2
H L L L L
M M L L H
L H L L VH
Table 2: Values in Table 1 fuzzified.
behavior of node 1.
Output refers to the
CPU CPU Memory Memory Rate N 2
N 1 N 2 N 1 N 2
H L L L H
M M L L L
L H L L VL
Table 3: Values in Table 1 fuzzified.
behavior of node 2.
Output refers to the
The initial population was obtained from all possible
fuzzy rules which contain only one linguistic antecedent
value. In this case, as shown in Figure 2, the total quantity
of linguistic terms is 5 (VL, L, M, H and VH). The amount
of linguistic variables is 2 (CPU and memory). Thus, the
initial number of rules is N =5∗ 2=⇒ N =10. For
each initial rule, it was calculated the consequent of their
respective class. The consequent class is also one of the
linguistic terms shown in Figure 2. As an example, if in
Figure 1, the load node C1 is 23%, then the fuzzy linguistic
term will be Low (L). If the load of the node C2 is 52%,
then fuzzy linguistic term will be fuzzy Medium (M).
After applying the technique described by Mansoori [10]
the rules are generated. Table 4 presents some of these rules.
Figure 2: Membership functions used for fuzzify the values in
Table 1.
No. Rule
1 if CPU 1 is LOW and MEMORY 1 is
MEDIUM then RAT E 1 is HIGH
2 if CPU 1 is LOW and CPU 2 is MEDIUM then
RAT E 1 is HIGH
Table 4: Some rules obtained after the learning process.
Starting from the generated rules, we created a Fuzzy
Inference System. The input variables are the values of the
system CPU and memory of each node. The membership
functions and fuzzy sets are described in Figure 2.
When the load balancer is working, COGNARE
constantly run fuzzy inference system. This step
recalculates the working rate of each node served by the
load balancer.
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