TY - CHAP
T1 - A Reality Check on Inference at Mobile Networks Edge
AU - Cartas, Alejandro
AU - Kocour, Martin
AU - Raman, Aravindh
AU - Leontiadis, Ilias
AU - Luque, Jordi
AU - Sastry, Nishanth
AU - Nuñez-Martinez, Jose
AU - Perino, Diego
AU - Segura, Carlos
PY - 2019/3/25
Y1 - 2019/3/25
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Edge computing
UR - http://www.scopus.com/inward/record.url?scp=85063915208&partnerID=8YFLogxK
U2 - 10.1145/3301418.3313946
DO - 10.1145/3301418.3313946
M3 - Conference paper
SN - 978-1-4503-6275-7
T3 - EdgeSys '19
SP - 54
EP - 59
BT - Proceedings of the 2Nd International Workshop on Edge Systems, Analytics and Networking
PB - ACM
CY - New York, NY, USA
ER -