  With rapid advances in containerization techniques, the serverless computing
model is becoming a valid candidate execution model in edge networking, similar
to the widely used cloud model for applications that are stateless, single
purpose and event-driven, and in particular for delay-sensitive applications.
One of the cloud serverless processes, i.e., the auto-scaling mechanism, cannot
be however directly applied at the edge, due to the distributed nature of edge
nodes, the difficulty of optimal resource allocation, and the delay sensitivity
of workloads. We propose a solution to the auto-scaling problem by applying
reinforcement learning (RL) approach to solving problem of efficient scaling
and resource allocation of serverless functions in edge networks. We compare RL
and Deep RL algorithms with empirical, monitoring-based heuristics, considering
delay-sensitive applications. The simulation results shows that RL algorithm
outperforms the standard, monitoring-based algorithms in terms of total delay
of function requests, while achieving an improvement in delay performance by up
to 50%.
