  The broad development and usage of edge devices has highlighted the
importance of creating resilient and computationally advanced environments.
When working with edge devices these desiderata are usually achieved through
replication and offloading. This paper reports on the design and implementation
of Workrs, a fault tolerant service that enables the offloading of jobs from
devices with limited computational power. We propose a solution that allows
users to upload jobs through a web service, which will be executed on edge
nodes within the system. The solution is designed to be fault tolerant and
scalable, with no single point of failure as well as the ability to accommodate
growth, if the service is expanded. The use of Docker checkpointing on the
worker machines ensures that jobs can be resumed in the event of a fault. We
provide a mathematical approach to optimize the number of checkpoints that are
created along a computation, given that we can forecast the time needed to
execute a job. We present experiments that indicate in which scenarios
checkpointing benefits job execution. The results achieved are based on a
working prototype which shows clear benefits of using checkpointing and restore
when the completion jobs' time rises compared with the forecast fault rate. The
code of Workrs is released as open source, and it is available at
\url{https://github.com/orgs/P7-workrs/repositories}. This paper is an extended
version of \cite{edge2023paper}.
