A Reality Check on Inference at Mobile Networks Edge

Alejandro Cartas, Martin Kocour, Aravindh Raman, Ilias Leontiadis, Jordi Luque, Nishanth Sastry, Jose Nuñez-Martinez, Diego Perino, Carlos Segura

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

20 Citations (Scopus)

Abstract

Edge computing is considered a key enabler to deploy Artificial Intelligence platforms to provide real-time applications such as AR/VR or cognitive assistance. Previous works show computing capabilities deployed very close to the user can actually reduce the end-to-end latency of such interactive applications. Nonetheless, the main performance bottleneck remains in the machine learning inference operation. In this paper, we question some assumptions of these works, as the network location where edge computing is deployed, and considered software architectures within the framework of a couple of popular machine learning tasks. Our experimental evaluation shows that after performance tuning that leverages recent advances in deep learning algorithms and hardware, network latency is now the main bottleneck on end-to-end application performance. We also report that deploying computing capabilities at the first network node still provides latency reduction but, overall, it is not required by all applications. Based on our findings, we overview the requirements and sketch the design of an adaptive architecture for general machine learning inference across edge locations.

Original languageEnglish
Title of host publicationProceedings of the 2Nd International Workshop on Edge Systems, Analytics and Networking
Place of PublicationNew York, NY, USA
PublisherACM
Pages54-59
Number of pages6
ISBN (Electronic)9781450362757
ISBN (Print)978-1-4503-6275-7
DOIs
Publication statusPublished - 25 Mar 2019

Publication series

NameEdgeSys '19
PublisherACM

Keywords

  • Artificial intelligence
  • Edge computing

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