Abstract:
Some representative 5G application scenarios regard geographic areas very far from the structured core network, but are characterized by the need for processing huge amou...Show MoreMetadata
Abstract:
Some representative 5G application scenarios regard geographic areas very far from the structured core network, but are characterized by the need for processing huge amount of data that cannot be transmitted to multi-access edge (MEC) facilities installed at the edge of that network. To this purpose, this paper proposes to extend a 5G network slice with a fleet of UAVs, each providing computing facilities, and for this reason referred to as MEC UAVs. The paper proposes a cooperation between MEC UAVs belonging to the same fleet based on job offloading, aiming at minimizing power consumption due to active computer elements providing MEC, job loss probability and queueing delay. A Reinforcement Learning (RL) approach is used to support the System Controller in its decisions. A numerical analysis is presented to evaluate achieved performance.
Published in: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Date of Conference: 29 April 2019 - 02 May 2019
Date Added to IEEE Xplore: 23 September 2019
ISBN Information: