  In an increasingly complex scenario for network management, a solution that
allows configuration in more autonomous way with less intervention of the
network manager is expected. This paper presents an evaluation of similarity
functions that are necessary in the context of using a learning strategy for
finding solutions. The learning approach considered is based on Case-Based
Reasoning (CBR) and is applied to a network scenario where different Bandwidth
Allocation Models (BAMs) behaviors are used and must be eventually switched
looking for the best possible network operation. In this context, it is
required to identify and configure an adequate similarity function that will be
used in the learning process to recover similar solutions previously
considered. This paper introduces the similarity functions, explains the
relevant aspects of the learning process in which the similarity function plays
a role and, finally, presents a proof of concept for a specific similarity
function adopted. Results show that the similarity function was capable to get
similar results from the existing use case database. As such, the use of
similarity functions with CBR technique has proved to be potentially
satisfactory for supporting BAM switching decisions mostly driven by the
dynamics of input traffic profile.
