Scheduling Offloading Decisions for Heterogeneous Drones on Shared Edge Resources

Multiple applications use autonomous drones to perform data collection and processing missions, which may involve heavyweight computations that need to be performed at runtime, rather than post-mission. The processing time (and thus also the total mission time) can be reduced by offloading such computations to nearby edge servers. However, these servers may have limited resources so that it is not possible to serve all offloading requests at the same time. In this case, the edge resources must be shared among the drones operating in the wider area in a fair way, so that every drone gets a share of the available processing capacity to reduce its mission time. In this work, we present different path and offload planning heuristics, which we evaluate for a wide range of mission scenarios with varying degrees of contention and for drones that are heterogeneous in terms of their flight and onboard processing capabilities. We show that the mission time of each drone can be significantly reduced, compared to the default case where all computations are performed onboard, while producing fair schedules that respect the heterogeneity of the drones and their missions.