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TUTCRIS

Peer to Peer Offloading with Delayed Feedback: An Adversary Bandit Approach

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
KustantajaIEEE
Sivut5035-5039
Sivumäärä5
ISBN (elektroninen)9781509066315
DOI - pysyväislinkit
TilaJulkaistu - 1 toukokuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Acoustics, Speech and Signal Processing - Barcelona, Espanja
Kesto: 4 toukokuuta 20208 toukokuuta 2020

Julkaisusarja

NimiICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Vuosikerta2020-May
ISSN (painettu)1520-6149

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
MaaEspanja
KaupunkiBarcelona
Ajanjakso4/05/208/05/20

Tiivistelmä

Fog computing brings computation and services to the edge of networks enabling real time applications. In order to provide satisfactory quality of experience, the latency of fog networks needs to be minimized. In this paper, we consider a peer computation offloading problem for a fog network with unknown dynamics. Peer competition occurs when different fog nodes offload tasks to the same peer FN. In this paper, the computation offloading problem is modeled as a sequential FN selection problem with delayed feedback. We construct an online learning policy based on the adversary multi-arm bandit framework to deal with peer competition and delayed feedback. Simulation results validate the effectiveness of the proposed policy.