Paper Info
Title
A Reality Check on Inference at Mobile Networks Edge
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.
Year
DOI
Venue
2019
10.1145/3301418.3313946
Proceedings of the 2nd International Workshop on Edge Systems, Analytics and Networking
Keywords
Field
DocType
Artificial Intelligence, Edge computing
Edge computing,Adaptive architecture,Bottleneck,Inference,Computer science,Node (networking),Software,Artificial intelligence,Deep learning,Performance tuning,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-4503-6275-7
3
0.42
References 
Authors
0
9
Name
Order
Citations
PageRank
Alejandro Cartas173.97
Martin Kocour230.42
Aravindh Raman3366.25
Ilias Leontiadis476144.38
Jordi Luque5104.64
Nishanth Sastry637531.46
José Núñez-Martínez78716.06
Diego Perino874050.54
Carlos Segura921621.44