On Performance VNF Load Prediction Models in Service Function Chaining

Yunyoung Cho, Seokwon Jang, Sangheon Pack

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

Abstract

One of the fundamental challenges in network function virtualization (NFV) is how to autonomously and dynamically allocate resources to virtualized network functions (VNFs) whose resource requirements frequently changed. To address this issue, several machine learning (ML)-based studies have been proposed, which predict the future loads of VNF in service function chaining (SFC) enabled networks. In this paper, we compare two prediction models: 1) basic long short term memory (LSTM) model that uses historical resource utilization of VNF to predict and 2) context and aspect embedded attentive target dependent LSTM (CAT-LSTM) model that leverages both historical utilization of VNF and its neighbor VNFs. Simulation results demonstrate that performance of basic LSTM is not affected by the number of service chain (SC) allocated in VNF. Meanwhile, the performance of CAT-LSTM is apparently degraded when multiple SCs are allocated in VNFs, which means prediction loss of CAT-LSTM increases from 4% to 6%.

Original languageEnglish
Title of host publicationICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publicationData, Network, and AI in the Age of Untact
PublisherIEEE Computer Society
Pages344-346
Number of pages3
ISBN (Electronic)9781728167589
DOIs
Publication statusPublished - 2020 Oct 21
Event11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of
Duration: 2020 Oct 212020 Oct 23

Publication series

NameInternational Conference on ICT Convergence
Volume2020-October
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Country/TerritoryKorea, Republic of
CityJeju Island
Period20/10/2120/10/23

Keywords

  • load prediction
  • machine learning
  • service function chaining
  • Virtualized network function

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

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