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Augmented Computing at the Edge Using Named Data Networking

  • Rustam Pirmagomedov
  • , Srikathyayani Srikanteswara
  • , Dmitri Moltchanov
  • , Gabriel Arrobo
  • , Yi Zhang
  • , Nageen Himayat
  • , Yevgeni Koucheryavy

Tutkimustuotos: KonferenssiartikkeliTieteellinenvertaisarvioitu

8 Sitaatiot (Scopus)
43 Lataukset (Pure)

Abstrakti

Edge computing is considered vital to IoT evolution, enabling the timely execution of various computational tasks for constrained devices utilizing external resources. The conventional host-based network architectures become a bottleneck for further development of edge computing, primarily when serving latencysensitive applications. Further, existing approaches do not exploit complex data correlations in the network layer for optimization. This paper demonstrates that Named Data Networking (NDN) has the potential to enable efficient support for mobile users offloading their time-sensitive computing tasks to edge servers. For this purpose, the NDN protocol was enhanced with a server selection procedure, capable of adjusting for the varying resource availability on edge servers. The results of the experiments show clear support for using NDN in these scenarios, with individual gains coming not just from Interest aggregation and caching, which are NDN features, but also from dynamic server selection.

AlkuperäiskieliEnglanti
Otsikko2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
KustantajaIEEE
Sivumäärä6
ISBN (elektroninen)9781728173078
DOI - pysyväislinkit
TilaJulkaistu - 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma IEEE Globecom Workshops - Virtual, Taipei, Taiwan
Kesto: 7 jouluk. 202011 jouluk. 2020

Julkaisusarja

Nimi2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

Conference

Conference IEEE Globecom Workshops
Maa/AlueTaiwan
KaupunkiTaipei
Ajanjakso7/12/2011/12/20

Julkaisufoorumi-taso

  • Jufo-taso 1

!!ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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