A Reality Check on Inference at Mobile Networks Edge
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU132061" target="_blank" >RIV/00216305:26230/19:PU132061 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/citation.cfm?doid=3301418.3313946" target="_blank" >https://dl.acm.org/citation.cfm?doid=3301418.3313946</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3301418.3313946" target="_blank" >10.1145/3301418.3313946</a>
Alternative languages
Result language
angličtina
Original language name
A Reality Check on Inference at Mobile Networks Edge
Original language description
Edge computing is considered a key enabler to deploy ArtificialIntelligence platforms to provide real-time applications such asAR/VR or cognitive assistance. Previous works show computingcapabilities deployed very close to the user can actually reduce theend-to-end latency of such interactive applications. Nonetheless,the main performance bottleneck remains in the machine learninginference operation. In this paper, we question some assumptionsof these works, as the network location where edge computing isdeployed, and considered software architectures within the frame-work of a couple of popular machine learning tasks. Our experimen-tal evaluation shows that after performance tuning that leveragesrecent advances in deep learning algorithms and hardware, net-work latency is now the main bottleneck on end-to-end applicationperformance. We also report that deploying computing capabilitiesat the first network node still provides latency reduction but, over-all, it is not required by all applications. Based on our findings, weoverview the requirements and sketch the design of an adaptivearchitecture for general machine learning inference across edgelocations.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2nd ACM International Workshop on Edge Systems, Analytics and Networking (EDGESYS '19)
ISBN
978-1-4503-6275-7
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
54-59
Publisher name
Association for Computing Machinery
Place of publication
Dressden
Event location
Dressden
Event date
Mar 25, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000470896200010