  Unmanned aerial vehicles (UAVs) have gained wide research interests due to
their technological advancement and high mobility. The UAVs are equipped with
increasingly advanced capabilities to run computationally intensive
applications enabled by machine learning techniques. However, because of both
energy and computation constraints, the UAVs face issues hovering in the sky
while performing computation due to weather uncertainty. To overcome the
computation constraints, the UAVs can partially or fully offload their
computation tasks to the edge servers. In ordinary computation offloading
operations, the UAVs can retrieve the result from the returned output.
Nevertheless, if the UAVs are unable to retrieve the entire result from the
edge servers, i.e., straggling edge servers, this operation will fail. In this
paper, we propose a coded distributed computing approach for computation
offloading to mitigate straggling edge servers. The UAVs can retrieve the
returned result when the number of returned copies is greater than or equal to
the recovery threshold. There is a shortfall if the returned copies are less
than the recovery threshold. To minimize the cost of the network, energy
consumption by the UAVs, and prevent over and under subscription of the
resources, we devise a two-phase Stochastic Coded Offloading Scheme (SCOS). In
the first phase, the appropriate UAVs are allocated to the charging stations
amid weather uncertainty. In the second phase, we use the $z$-stage Stochastic
Integer Programming (SIP) to optimize the number of computation subtasks
offloaded and computed locally, while taking into account the computation
shortfall and demand uncertainty. By using a real dataset, the simulation
results show that our proposed scheme is fully dynamic, and minimizes the cost
of the network and UAV energy consumption amid stochastic uncertainties.
