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Title: NetML: An NFV Platform with Efficient Support for Machine Learning Applications
Real-time applications such as autonomous and connected cars, surveillance, and online learning applications have to train on streaming data. They require low-latency, high throughput machine learning (ML) functions resident in the network and in the cloud to perform learning and inference. NFV on edge cloud platforms can provide support for these applications by having heterogeneous computing including GPUs and other accelerators to offload ML-related computation. GPUs provide the necessary speedup for performing learning and inference to meet the needs of these latency sensitive real-time applications. Supporting ML inference and learning efficiently for streaming data in NFV platforms has several challenges. In this paper, we present a framework, NetML, that runs existing ML applications on an heterogeneous NFV platform that includes both CPUs and GPUs. NetML efficiently transfers the appropriate packet payload to the GPU, minimizing overheads, avoiding locks, and avoiding CPU-based data copies. Additionally, NetML minimizes latency by maximizing overlap between the data movement and GPU computation. We evaluate the efficiency of our approach for training and inference using popular object detection algorithms on our platform. NetML reduces the latency for inferring images by more than 20% and increases the training throughput by 30% while reducing CPU utilization compared to other state-of-the-art alternatives.  more » « less
Award ID(s):
1763929
NSF-PAR ID:
10120151
Author(s) / Creator(s):
;
Date Published:
Journal Name:
2019 IEEE Conference on Network Softwarization (NetSoft)
Page Range / eLocation ID:
396 to 404
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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