  Communication networks have evolved from specialized, research and tactical
transmission systems to large-scale and highly complex interconnections of
intelligent devices, increasingly becoming more commercial, consumer-oriented,
and heterogeneous. Propelled by emergent social networking services and
high-definition streaming platforms, network traffic has grown explosively
thanks to the advances in processing speed and storage capacity of
state-of-the-art communication technologies. As "netizens" demand a seamless
networking experience that entails not only higher speeds, but also resilience
and robustness to failures and malicious cyber-attacks, ample opportunities for
signal processing (SP) research arise. The vision is for ubiquitous smart
network devices to enable data-driven statistical learning algorithms for
distributed, robust, and online network operation and management, adaptable to
the dynamically-evolving network landscape with minimal need for human
intervention. The present paper aims at delineating the analytical background
and the relevance of SP tools to dynamic network monitoring, introducing the SP
readership to the concept of dynamic network cartography -- a framework to
construct maps of the dynamic network state in an efficient and scalable manner
tailored to large-scale heterogeneous networks.
