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Dimensioning resources of Network Slices for energy-performance trade-off

  • CNRS UMR 5157 SAMOVAR

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Within network slicing, Virtual Network Embedding has been vastly studied, i.e., deciding in which physical nodes and links to place virtual functions and links. However, the performance of slices does not only depend on where virtual functions and links are placed, but also on how much resources they can use, which has been mostly neglected in the literature. We thus propose a method for optimal resource dimensioning, via dimensioning capacities of multiple Jackson networks (one per slice) co-existing in the same resource-constrained network. Despite the long history of Jackson networks, we are the first, to the best of our knowledge, to model such a problem. The objective is to minimize energy consumption while satisfying the latency requirements of heterogeneous service providers. We show numerically that our solution is able to achieve both goals, differently from classic approaches, which assume that the amount of resources assigned to slices is fixed a-priori.

Original languageEnglish
Title of host publication2022 IEEE Symposium on Computers and Communications, ISCC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665497923
DOIs
Publication statusPublished - 1 Jan 2022
Event27th IEEE Symposium on Computers and Communications, ISCC 2022 - Rhodes, Greece
Duration: 30 Jun 20223 Jul 2022

Publication series

NameProceedings - IEEE Symposium on Computers and Communications
Volume2022-June
ISSN (Print)1530-1346

Conference

Conference27th IEEE Symposium on Computers and Communications, ISCC 2022
Country/TerritoryGreece
CityRhodes
Period30/06/223/07/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Jackson Networks
  • Network Slicing
  • Optimization
  • Resource allocation

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