Situated vs. global aggregation schemes for autonomous management systems

In the context of autonomous network management, the Autonomic Managers (AMs) need to collect management information from other elements in order to infer an overall state of the network considered by the decision making process. Two concurrent strategies are commonly used to achieve this operation. On one hand, approaches based on a situated view only gather information in a bounded neighborhood, thus providing a high reactivity to AMs for control operations. On the other hand, approaches based on a global view provide a good accuracy at the cost of a larger convergence time. Being able to choose the best approach in a given context is crucial to ensure the efficiency of an autonomous management system. Thus, in this paper, we perform an exhaustive performance analysis of these approaches by considering typical schemes of both of them, namely a one-hop and two-hops situated view against gossip- and tree-based global aggregation schemes. Metrics we consider are the convergence time, communication and computation cost, scalability and the accuracy of estimated aggregates. Given them, we show under which conditions an approach outperforms the others.