  Serverless computing has emerged as a new paradigm for running short-lived
computations in the cloud. Due to its ability to handle IoT workloads, there
has been considerable interest in running serverless functions at the edge.
However, the constrained nature of the edge and the latency sensitive nature of
workloads result in many challenges for serverless platforms. In this paper, we
present LaSS, a platform that uses model-driven approaches for running
latency-sensitive serverless computations on edge resources. LaSS uses
principled queuing-based methods to determine an appropriate allocation for
each hosted function and auto-scales the allocated resources in response to
workload dynamics. LaSS uses a fair-share allocation approach to guarantee a
minimum of allocated resources to each function in the presence of overload. In
addition, it utilizes resource reclamation methods based on container deflation
and termination to reassign resources from over-provisioned functions to
under-provisioned ones. We implement a prototype of our approach on an
OpenWhisk serverless edge cluster and conduct a detailed experimental
evaluation. Our results show that LaSS can accurately predict the resources
needed for serverless functions in the presence of highly dynamic workloads,
and reprovision container capacity within hundreds of milliseconds while
maintaining fair share allocation guarantees.
