Uma Proposta para a Gerência Autonômica e Escalável de Redes de Computadores

Network services diversity, huge network size and technology heterogeneity are currently computer network characteristics that gradually induce network management to become a highly complex task for administrators. In this context, the Autonomic Management Systems (AMS) are being investigated as possible approaches capable to deal with this inherent complexity. In effect, it is expected that new autonomic systems and solutions could, among other functionalities, both configure and optimize network resources keeping network performance characteristics on a scalable way. This paper addresses, fundamentally, the scalability problem faced by current AMS implementations. In effect, it is expected that autonomic solutions could be computed, preferably, on-the-fly and, as such, can effectively substitute human intervention in network management. We argue that a network partitioning strategy can be applied to manage the scalability problem while considering a set of requirements specified by the network administrator (QoS parameters and execution time). This thesis addresses the set of basic network management requirements considered by the proposal, details the partitioning method and, finally, evaluates the related scalability issues considering a self-managed framework (AMS) in diverse scalable scenarios. Results points to the feasibility of the partitioning method in scenarios with different traffic matrix allocation and, beyond that, under new topologies resulting from failures