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Using remote GPU virtualization techniques to enhance edge computing devices
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AI
DOI：10.1016/j.future.2022.12.038.png)
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Abstract
En 中文
The Internet of Things (IoT) is driving the next economic revolution where the main actors are both data and immediacy. The IoT ecosystem is increasingly generating large amounts of data that are created but never analyzed. Efficient big data analysis in IoT infrastructures is becoming mandatory to transform this data deluge into meaningful information. Edge computing is proving to be a compelling alternative for enabling computing capabilities at the edge of the network. These computing capabilities could help in transforming the generated data into useful information. However, the edge computing platforms available on the market are low-power devices with limited computing horsepower. In this paper, we present a novel approach to providing computing resources to edge devices without penalizing their power consumption by using remotely virtualized GPUs. We evaluate this hardware environment by executing a computational-intensive clustering algorithm called Fuzzy Minimals (FM). Our results show that using a remotely virtualized GPU on the edge device provides a 3.2x speed-up factor compared to the local counterpart version. Moreover, we report up to 30% reduction in power consumption and up to 80% of energy savings at the edge device, delegating the GPU workload to the backend, transparently to the programmer.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Machine learning
Clustering algorithms
Edge computing
Remote virtualization
Virtualized GPUs
IoT
Journal
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IF:
6.1
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6.8K
Citations:
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