A Task Placement System for Face Recognition Applications in Edge Computing

Hayata Satake, Ryotaro Tani, Hiroshi Shigeno

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

Abstract

Mobile devices have certain restrictions such as processing power and network bandwidth. Edge computing can reduce load and delay by migrating the parts of application processing from mobile devices to edge servers. Edge computing is a method that extends cloud computing to the edge of network. Conventional schemes of face recognition based on edge computing can reduce the amount of network transmission data between the edge servers and the cloud servers by preprocessing data on the edge servers. However, the schemes execute each task on a certain resource, thus application response time may increase according to the conditions of computational resources and network. Hence, a task placement system considering the conditions of computational resources and network is necessary. This paper proposes a task placement system for face recognition applications in edge computing. The proposed system calculates estimated response time of possible task placements based on the task placement decision formula. The formula parameterizes power and CPU utilization of computational resources and delay and bandwidth of network. The task placement that minimizes estimated response time is determined. We implemented a prototype of the proposed task placement system and evaluated its performance through experiment.

Original languageEnglish
Title of host publication2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728138930
DOIs
Publication statusPublished - 2020 Jan
Event17th IEEE Annual Consumer Communications and Networking Conference, CCNC 2020 - Las Vegas, United States
Duration: 2020 Jan 102020 Jan 13

Publication series

Name2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020

Conference

Conference17th IEEE Annual Consumer Communications and Networking Conference, CCNC 2020
CountryUnited States
CityLas Vegas
Period20/1/1020/1/13

Keywords

  • Distributed Processing
  • Edge Computing
  • Face Recognition
  • Task Placement

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Media Technology
  • Communication

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  • Cite this

    Satake, H., Tani, R., & Shigeno, H. (2020). A Task Placement System for Face Recognition Applications in Edge Computing. In 2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020 [9045194] (2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCNC46108.2020.9045194