Proactive Resource Orchestration Framework for Cloud/Fog Platform

Somnath Mazumdar, Thomas Dreibholz

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

11 Downloads (Pure)

Abstract

Cloud computing makes complex processing an off-premise activity by offering software- and hardware-based services using standard security protocols over the Internet. It has been seen that the cloud is not ideal for latency-sensitive applications. Thanks to the current growth of network communication and infrastructure, fog adds a computing resource delegation model between the user and the cloud. Fog aims to improve latency-sensitive applications support. Here, we propose one unified, proactive resource orchestration framework from a cloud/fog service provider perspective. The framework consists of a predictor and a resource allocator module. Users subscribe to these resources to execute their applications. The framework is modular and does not require application-specific information. A service provider can customise each module. We have presented the framework prototype by showing each module's simulated performance results using the parameters of our cloud/fog research testbed.
Original languageEnglish
Title of host publicationISCC 2023 - 28th IEEE Symposium on Computers and Communications : Computers and Communications for the Benefits of Humanity
Number of pages7
PublisherIEEE
Publication dateJul 2023
Pages259-265
ISBN (Print)9798350300482
ISBN (Electronic)9798350300482
DOIs
Publication statusPublished - Jul 2023
Event28th IEEE Symposium on Computers and Communications: Computers and Communications for the Benefits of Humanity - Gammarth, Tunisia
Duration: 9 Jul 202312 Jul 2023
Conference number: 28
https://ieeexplore.ieee.org/xpl/conhome/10217219/proceeding

Conference

Conference28th IEEE Symposium on Computers and Communications
Number28
Country/TerritoryTunisia
CityGammarth
Period09/07/202312/07/2023
Internet address

Keywords

  • Cloud
  • Fog
  • Neural network
  • Prediction
  • Wavelet

Cite this