Various centralized, distributed or cooperative management
systems have been proposed to address the demands of
wireless telecommunication networks. However, considering the size,
complexity and heterogeneity that those networks will have in the
future, current solutions either do not scale properly, or have no
support for automation, or lack of the flexibility and simple control
that operators will need for managing future networks in a costeffective
way. To address this problem, we designed Omega, a
distributed and policy-based network management system that uses
rich knowledge-modeling techniques to develop self-configuration
capabilities. Omega also implements a novel conflict-resolution
method that uses high-level goals and machine learning techniques
to optimize its policy-based decisions. Using simulations, in this
paper we show how Omega reduces the impact of a node crash on
the overall availability of a radio access network by optimizing the
lists of neighboring cells of the nodes in the vicinity.
