  Edge computing is an emerging paradigm to enable low-latency applications,
like mobile augmented reality, because it takes the computation on processing
devices that are closer to the users. On the other hand, the need for highly
scalable execution of stateless tasks for cloud systems is driving the
definition of new technologies based on serverless computing. In this paper, we
propose a novel architecture where the two converge to enable low-latency
applications: this is achieved by offloading short-lived stateless tasks from
the user terminals to edge nodes. Furthermore, we design a distributed
algorithm that tackles the research challenge of selecting the best executor,
based on real-time measurements and simple, yet effective, prediction
algorithms. Finally, we describe a new performance evaluation framework
specifically designed for an accurate assessment of algorithms and protocols in
edge computing environments, where the nodes may have very heterogeneous
networking and processing capabilities. The proposed framework relies on the
use of real components on lightweight virtualization mixed with simulated
computation and is well-suited to the analysis of several applications and
network environments. Using our framework, we evaluate our proposed
architecture and algorithms in small- and large-scale edge computing scenarios,
showing that our solution achieves similar or better delay performance than a
centralized solution, with far less network utilization.
