  Pure Edge computing (PEC) aims to bring cloud applications and services to
the edge of the network to support the growing user demand for time-sensitive
applications and data-driven computing. However, mobility and limited
computational capacity of edge devices pose challenges in supporting some
urgent and computationally intensive tasks with strict response time demands.
If the execution results of these tasks exceed the deadline, they become
worthless and can cause severe safety issues. Therefore, it is essential to
ensure that edge nodes complete as many latency-sensitive tasks as possible.
\\In this paper, we propose a Robust Adaptive Workload Orchestration
(R-AdWOrch) model to minimize deadline misses and data loss by using priority
definition and a reallocation strategy. The results show that R-AdWOrch can
minimize deadline misses of urgent tasks while minimizing the data loss of
lower priority tasks under all conditions.
