SEKE 2012 Proceedings - Knowledge Systems Institute

In this way, COGNARE could change dynamically
working rates for each node of the system.
5. Case study and evaluation of results
We used three computers in the same network in order
to test the technique proposed in this work. The three
computers have distinct setups. The Tables 5 and 6 show
these setups. The resources of the computer named C 3 are
lower than the other two. This fact helped to check the load
balancer’s efficiency in situations where exist differences
among hardwares.
(a) Load balancer with “Round Robin”
Name Cores CPU RAM
C 1 4 800 MHz 4GB
C 2 4 800 MHz 6GB
C 3 2 1200 MHz 2GB
Table 5: Configuration of the computers used in our tests.
(b) Load balancer with COGNARE
Figure 3: Behavior of nodes in the tests performed.
As illustrated in Figure 1, the computer C 1 generates
requests to the load balancer and COGNARE. The
computers C 2 and C 3 act like balancer nodes. The Table
6 describes each computer used in our tests.
Name Setup
C 1 AMD Phenom(tm) II X4 955 Processor - 4 GB
RAM
C 2 Intel(R) Core(TM) i7-2620M CPU @ 2.70GHz -
6GBRAM
C 3 AMD Turion(tm) 64 X2 Mobile Technology
TL-50 1.2GHZ - 2GB RAM
Table 6: Computers used in our tests.
In the performed tests, 2000 requests were sent to the
load balancer. So, the total time to process all requests
was measured. The average speed to process requests
per minute was calculated. Therefore, a high speed
means a most efficient system. The test was repeated 20
times. Among this 20 times, 10 times were executed with
COGNARE changing dynamically the load of the nodes.
The another 10 times were executed using a Round-Robin
algorithm, where the allocation was made disregarding the
differences between computers nodes.
After the tests, the average speeds were calculated. The
Table 7 shows these values.
Test Speed (requests per minute)
COGNARE 1484
Round Robin 804
Table 7: Averages of the tests performed.
As showed in the Table 7, the load balancer with
COGNARE improved the system, increasing its processing
capacity. This happened because the weaker hardware
received fewer requests than the other hardware.
The Figure 3 shows the behavior of the nodes in the
our tests. Using Round-Robin (a), the computer 1 works
very less than the computer 2 and the maximum CPU’s
usage was 62.5%. In the case (b), using COGNARE, the
system’s performance was improved. The maximum CPU’s
usage was 42%. Moreover, despite the differences between
the hardwares, using COGNARE, the computers showed a
similar behavior.
6. Conclusion
This paper presented the use of Evolutionary Learning
and Fuzzy Logic for dynamic allocation of resources in a
load balancer.
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