TY - GEN
T1 - Augmented Computing at the Edge Using Named Data Networking
AU - Pirmagomedov, Rustam
AU - Srikanteswara, Srikathyayani
AU - Moltchanov, Dmitri
AU - Arrobo, Gabriel
AU - Zhang, Yi
AU - Himayat, Nageen
AU - Koucheryavy, Yevgeni
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - edge computing
KW - Future Internet
KW - ICN
KW - NDN
U2 - 10.1109/GCWkshps50303.2020.9367585
DO - 10.1109/GCWkshps50303.2020.9367585
M3 - Conference contribution
AN - SCOPUS:85102934476
T3 - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
BT - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PB - IEEE
T2 - IEEE Globecom Workshops
Y2 - 7 December 2020 through 11 December 2020
ER -