AI-Driven Digital Twins for Tasks Offloading in 6G UAV-Aided MEC Networks

Nowadays, the functional integration of Unmanned Aerial Vehicles (UAVs) as flying computing nodes with terrestrial networks is rapidly emerging as promising and viable solution to enhance performance or lower drawbacks arising by unpre- dictable traffic load congestion occurrences. In particular, this paper considers a UAV-Aided Mobile Edge Computing system, in which heterogeneous traffic flows with different quality of service constraints, have to be offloaded on processing nodes consisting of terrestrial and flying edge computing nodes. Towards this goal, the paper proposes a matching algorithm to perform proper offloading strategies. The matching algorithm designed provides decisions on the basis of per-flow end-to-end delay bounds formulated by resorting to the combined application of stochastic network calculus and martingale envelopes theory. Furthermore, matching stability has been theoretically discussed and numerical results have been provided in order to highlight the validity of the stochastic framework proposed in fitting the actual network behavior, considering also different state-of-the- art offloading alternatives.