Towards service discovery and invocation in data-centric edge networks

Spyridon Mastorakis, Abderrahmen Mtibaa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

The efforts exploring Named Data Networking (NDN) have mainly focused on addressing the lack of scalable data distribution by today's Internet. In this paper, we argue that NDN offers a richer environment for edge computing applications. We consider a scenario, where applications need to discover the services running in the edge network. We demonstrate the design and implementation of a distributed service discovery mechanism over NDN through an example use-case of a mobile application for vision impairment patient. The paper discusses three main edge computing challenges, namely service discovery, service invocation, and user mobility management, to highlight NDN's architectural advantages for edge computing systems. Experimental results show that our framework design can effectively utilize the available resources at the network edge, being able to satisfy 95-98% of mobile users' service requests.

Original languageEnglish (US)
Title of host publication27th IEEE International Conference on Network Protocols, ICNP 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728127002
DOIs
StatePublished - Oct 2019
Event27th IEEE International Conference on Network Protocols, ICNP 2019 - Chicago, United States
Duration: Oct 7 2019Oct 10 2019

Publication series

NameProceedings - International Conference on Network Protocols, ICNP
Volume2019-October
ISSN (Print)1092-1648

Conference

Conference27th IEEE International Conference on Network Protocols, ICNP 2019
Country/TerritoryUnited States
CityChicago
Period10/7/1910/10/19

Keywords

  • Edge Computing
  • Named-Data Networking
  • Service Discovery
  • Service Invocation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Fingerprint

Dive into the research topics of 'Towards service discovery and invocation in data-centric edge networks'. Together they form a unique fingerprint.

Cite this