SEKE 2012 Proceedings - Knowledge Systems Institute

state GA [11] [12] that aims the generation of a number Q
of rules per class, following the Cooperative-Competitive
Genetic Learning approach [8]. The generations (children)
are finite and limited to the extent of the problem. The
algorithm aims to keep the same amount Q of rules for
each class. The fuzzy rules if-then that classify a particular
problem with n attributes are represented in the following
form:
R j : If x 1 is A j1 ...and x n is A jn , then class C j (1)
forj=1,2,3,...N
where X =[x 1 ,x 2 ,...x n ] is the vector of input patterns,
A ji (i = 1, 2,...,n) are the linguistic terms used and
represent the antecedents of the rule R j . C j is the
consequent class R j and N is the fixed number of fuzzy
rules necessary to classify the input patterns. This fuzzy
classification rule R j can also be expressed as A j ⇒ C j ,
where A j are all the antecedents linguistic terms of the rule
and C j is the class that this rule classifies.
3. Load Balancer
Load balancing [5] is a technique used to evenly
distribute the workload between two or more resources,
such as network, CPU, hard drives, etc.. Its objective is to
optimize the use of these resources, maximize performance,
minimize response time and avoid overloading. As
illustrated in Figure 1, the balancer is able to split requests
among all nodes.
Two versions of load balancing problem have been
investigated in the literature: static and dynamic [4]. The
static version divides the workload equally among nodes,
regardless of their individually load. The dynamic version
changes the workload of a node as it increases. Therefore,
this last type of balancer performs real time measurements
of node workload in order to keep the whole system
workload balanced.
Generally, load balancers use a “Round-Robin” task
scheduler [14] [15]. In “Round-Robin” task schedulers, the
tasks are stored in a circular queue. The scheduler runs this
queue and allocates the resource for each of these tasks.
Thus, each task has a probability of being executed equal
to 1 n
, where n is the total number of tasks to be allocated.
Therefore, there may be an overload on the nodes due
to several factors. One of these factors, which often
occurs, is when a particular request causes an excessive
consumption of resources, for example, when a very large
file is transmitted. The node that receives this file will
suffer a significant increase in its load which, inevitably
will cause an imbalance. Another factor that may also
Figure 1: Scheme for use and connection of computers used
in our tests.
occur, is when nodes are not identical, i.e., when there
are different hardware receiving files. In this case, if the
same amount of requests is sent to each of nodes, those
who have small amounts of resources such as processing
capacity and memory will work in their operating limits,
causing a significant slowdown or even may fail.
4. Evolutionary Learning and Fuzzy Logic
employed in a Load Balancer
Evolutionary Learning (EA) [2] is a machine learning
technique, which uses elements of Evolutionary
Computation, to extract knowledge from a database.
Generally, this knowledge is represented by if-then type
rules. These rules are easily understood by humans
and easily processed by machines. Several branches of
Evolutionary Computation such as Genetic Algorithms,
Evolution Strategy, Evolutionary Programming, Particle
Swarm, among others have been used in order to extract
this knowledge in the form of rules.
A Fuzzy Rule Based System [8] [9] (FRBS) is composed
of a Knowledge Base (KB), which contains information
in the form of if-then fuzzy rules, the input layer, which
contains the fuzzification interface, the fuzzy inference
system, which together with the knowledge base performs
inference from the input and output layer, which contains
the defuzzification interface.
The fuzzy rules contained in the Knowledge Base have
the following structure: “if a set of conditions are met
(antecedent of the rule ) then a set of consequences can be
inferred (consequence of the rule).”
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