SEKE 2012 Proceedings - Knowledge Systems Institute

As described in the previous sections, agents have been
installed in each of the nodes. The aim of these agents was
to measure the CPU and memory utilization and to send this
information to COGNARE.
From the information regarding the status of the nodes,
we extracted the fuzzy rules. These rules are used in a Fuzzy
Inference System to change the rate of load of each node.
We believe that the main contribution of this proposal
is the ability the system got with COGNARE to learn in
advance the rules about its behavior. This previous learning
have significantly reduced the workload during execution
of the balancer, which caused a significant increase in
performance of the system as a whole.
In future works, we plan to use other algorithms
related to the Evolutionary Learning, such HIDER [1]
[2], NSGA-II [6], TARGET [7], among others to improve
COGNARE’s learning process. We are also planning to
do other experiments. This time we intend to use a larger
number of nodes in order to better capture the efficiency of
COGNARE.
The results of our experiments have revealed that
COGNARE is capable of increasing the performance of
a load balancer. Because of these results, COGNARE, is
already in use, balancing the load of a high-demand system,
used by Government of Goiás 2 .
7. Acknowledgment
The authors wish to thank Fundação de Amparo à
Pesquisa do Estado de Goiás - FAPEG and the Centrais
Elétricas de Goiás - CELG by the grant which funded this
project.
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2 Goiás is one of 27 Federal Units of Brazil.
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