SEKE 2012 Proceedings - Knowledge Systems Institute

Evolutionary Learning and Fuzzy Logic applied to a Load Balancer ∗
Francisco Calaça Xavier
Instituto de Informática
Universidade Federal de Goiás
chicocx@gmail.com
Max Gontijo de Oliveira
Instituto de Informática
Universidade Federal de Goiás
maxopala@gmail.com
Cedric L. de Carvalho
Instituto de Informática
Universidade Federal de Goiás
cedric@inf.ufg.br
Abstract
Load balancing is a fundamental requirement in the
development of parallel and distributed applications. The
emergence of computational grids increased the demand
for more efficient load balancers. Fuzzy Logic and
Evolutionary Learning shown to be effective in order to
increase the performance of a load balancer. This paper
presents a Fuzzy Inference System capable to assist a load
balancing in a computational grid. Evolutionary Learning
was used to generate rules for an Inference System, which
evaluated the behavior of nodes when they are subjected to
a workload. The obtained results proved that it is possible
to improve the performance of a load balancer using
a Fuzzy Inference System when the rules are previously
generated according to a particular behavior of the system.
Keywords: Evolutionary Learning, Fuzzy Inference
System, Load Balance
1. Introduction
The scalability problem appears with the increasing use
of information systems. One way to scale these systems is
by using load balancers.
This paper proposes the use of a dynamic load balancer.
Software agents [3] are installed in the nodes that monitor
the state of load and transmit this data to the load balancer.
In load balancer, this state of load is used by a Fuzzy
Inference System to change dynamically the rate of requests
sent to each of these nodes.
In this proposal, initially we used Evolutionary Learning
[2] to learn fuzzy rules according to the behavior of nodes.
Subsequently, these rules were used in a Fuzzy Inference
System that dynamically changes the behavior of the load
balancer.
The rest of this paper is organized as follows. Initially,
in Section 2, some related works are described. Section
∗ Sponsored by Fundação de Amparo à Pesquisa do Estado de Goiás -
FAPEG and Centrais Elétricas de Goiás - CELG
3 introduces the concept of load balancing. Section 4
describes the Artificial Intelligence techniques used to build
the load balancer developed in this work. In Section 5, a
case study and the results obtained with our experiments is
presented. Finally, we conclude the work in Section 6.
2. Related Work
Salehi et al [13] proposed the application of the ant
colony technique in a load balancer. In this case, intelligent
ants react proportionally to the conditions of the balancer’s
nodes. The load balancing is the result of the interaction
between these ants over the nodes.
In the approach proposed by Salehi et al, if a node gets
overloaded, it will create a new ant that will balance the load
by sending the requests to less loaded nodes. This step will
lead to balance the load between the nodes.
According to Wang et al [16], hosting databases on
virtual machines (VMs) has great potential to improve
efficiency of resource use and eases deployment of
databases.
The work of Wang et al [16] considers the problem
of allocating resources on demand to a virtual machine
running a database that serves data to a large amount of
dynamic and complex queries according to some Quality
of Service(QoS) 1 requirements.
To solve this problem, it was proposed an autonomous
resource management approach. This approach uses a fuzzy
modeling to capture and evaluate the performance of a
VM that hosts a database. Its goal is to take decisions
regarding resource requirements for each virtual machine,
anticipating their needs and changing dynamically work
loads. The proposed solution results demonstrated that CPU
and I/O disk can be efficiently allocated to the VMs running
the database in order to meet QoS requirements.
Steady-State Genetic Algorithm for Extracting Fuzzy
Classification Rules From Data (SGERD) [10] is a steady
1 Quality of service (QoS) refers to several aspects that allow
evaluating the quality of information transport in computer networks and
communication systems.
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