A Combined Stochastic Network Calculus and Matching Theory Approach for Computational Offloading in a Heterogenous MEC Environment

Nowadays, the functional integration of Unmanned Aerial Vehicles (UAVs) as flying computing nodes with terrestrial networks is rapidly emerging as a promising and viable solution to enhance performance or lower drawbacks arising from unpredictable traffic load congestion occurrences. In particular, this paper considers a UAV-Aided Multiple Access 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 an efficient offloading strategy. In particular, the proposed matching algorithm 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. Numerical results highlight the validity of the proposed stochastic framework in terms of both reliability, i.e., the probability with which the per-flow end-to-end delay is lower than the corresponding deadline, and its ability to fit the actual network behavior. For comparison purposes, the Boole bound is formulated, and a greedy algorithm is developed to compare the matching strategy designed.