This paper is motivated by the emerging vision of an automated and data-driven optimization of communication networks, making it possible to fully exploit the flexibilities offered by modern network technologies and heralding an era of fast and self-adjusting networks. We build upon our recent study of machine-learning approaches to (statically) optimize resource allocations based on the data produced by network algorithms in the past. We take our study a crucial step further by considering dynamic scenarios: scenarios where communication patterns can change over time. In particular, we investigate network algorithms which learn from the traffic distribution (the feature vector ), in order to predict global network allocations (a multi-label problem). As a case study, we consider a well-studied k-median problem arising in Software-Defined Networks, and aim to imitate and speedup existing heuristics as well as to predict good initial solutions for local search algorithms. We compare different machine learning algorithms by simulation and find that neural network can provide the best abstraction, saving up to two-thirds of the algorithm runtime.

Top