A Steiner tree-based verification approach for handling topology changes in self-organizing networks

In today's Self-Organizing Networks (SONs) we differentiate between closed-loop functions, which have a predefined absolute goal, and such that form an action plan that achieves a high expected utility. Both function types perform changes to Configuration Management (CM) parameters, but only the second type may re-adapt the action plan in order to maximize the utility. A SON verification approach is one member of this particular function class. It is seen as a special type of anomaly detection that divides the network into sets of cells, triggers an anomaly detection algorithm for those sets, and finally generates CM undo actions for the abnormally performing cells. Unfortunately, one of the challenges verification strategies are facing are network topology changes. Typically, cells are switched on or off when energy saving features are enabled. However, enabling or disabling cells can negatively influence a verification mechanism which may create a suboptimal action plan or even blame certain CM changes that actually did not harm performance. In order to overcome this issue, we present an approach that is based on Steiner trees. In graph theory, a Steiner tree is a Minimum Spanning Tree (MST) whose costs can be reduced by adding additional vertexes to the graph. We use this tree to filter out anomalies caused by topology adjustments and such induced by other CM changes. In this paper, we also evaluate the proposed solution in several scenarios. First, in a simulation study we evaluate the functions that are used to build the Steiner tree. Second, we show how it positively affects the network performance when having concurrent CM and topology changes.