| dc.description.abstract | The development of novel use cases in beyond-5G and 6G networks will rely, among other aspects, on the availability of computing resources at the edge, therefore enabling the realization of applications that are both computationally demanding and latency constrained, such as Mobile Augmented Reality (MAR). Indeed, due to
end devices’ intrinsic constraints on computation capabilities and battery, newer MAR applications require offloading their most demanding tasks. However, the constrained nature of edge resources implies that these tasks should be carefully allocated at the edge network in order to guarantee satisfactory Quality of Experience to end-users. In this context, we analyze the edge operator’s resource allocation to support the energy-aware offloading of MAR tasks at the edge of the cellular network with the goal of not only maximizing service acceptance (i.e., revenue), but also optimizing the operator’s business utility, which depends on its carbon footprint
and the profit of operating the service. We leverage Deep Reinforcement Learning to propose an efficient model to operate the edge resource allocation that can adapt to different utilities. | es |
