  The Industry 4.0 revolution has been made possible via AI-based applications
(e.g., for automation and maintenance) deployed on the serverless edge (aka
fog) computing platforms at the industrial sites -- where the data is
generated. Nevertheless, fulfilling the fault-intolerant and real-time
constraints of Industry 4.0 applications on resource-limited fog systems in
remote industrial sites (e.g., offshore oil fields) that are uncertain,
disaster-prone, and have no cloud access is challenging. It is this challenge
that our research aims at addressing. We consider the inelastic nature of the
fog systems, software architecture of the industrial applications
(micro-service-based versus monolithic), and scarcity of human experts in
remote sites. To enable cloud-like elasticity, our approach is to dynamically
and seamlessly (i.e., without human intervention) federate nearby fog systems.
Then, we develop serverless resource allocation solutions that are cognizant of
the applications' software architecture, their latency requirements, and
distributed nature of the underlying infrastructure. We propose methods to
seamlessly and optimally partition micro-service-based application across the
federated fog. Our experimental evaluation express that not only the elasticity
is overcome in a serverless manner, but also our developed application
partitioning method can serve around 20% more tasks on-time than the existing
methods in the literature.
