Using evolutionary dynamic optimization for monitor selection in highly dynamic communication infrastructures.

In this paper, we address the problem of applying evolutionary dynamic optimization of network monitoring to highly dynamic communication network infrastructures. One major challenge of modern communication networks is the increasing volatility due to, e.g., changing availability of nodes and links, load of paths, or attacks. While optimization of those dynamic networks has been an important application area since decades, new developments in the area of network function virtualization and software defined network facilitate a completely new level of automated dynamic network optimization. Especially in mobile networks, changes can be observed to appear swiftly. Thus, using population-based heuristics becomes challenging as reevaluation of all candidate solutions may become time-wise impossible and operations need to rely on possibly obsolete fitness values. Here, an established method has been applied to solve the dynamic monitor selection problem on multiple real-world problem instances using a different simulated level of change. Statistically significant results of the proposed method have been compared to the performance of a best-of-multiple selection local search (EMS LS) heuristic. As the results show, optimization reaches results of high quality even under difficult circumstances.