  This study is a first attempt to experimentally explore the range of
performance bottlenecks that 5G mobile networks can experience. To this end, we
leverage a wide range of measurements obtained with a prototype testbed that
captures the key aspects of a cloudified mobile network. We investigate the
relevance of the metrics and a number of approaches to accurately and
efficiently identify bottlenecks across the different locations of the network
and layers of the system architecture. Our findings validate the complexity of
this task in the multi-layered architecture and highlight the need for novel
monitoring approaches that intelligently fuse metrics across network layers and
functions. In particular, we find that distributed analytics performs
reasonably well both in terms of bottleneck identification accuracy and
incurred computational and communication overhead.
