Cost-Driven Offloading for DNN-based Applications over Cloud, Edge and End Devices

Lin, Bin; Huang, Yinhao; Zhang, Jianshan; Hu, Junqin; Chen, Xing; Li, Jun

doi:10.48550/arxiv.1907.13306

Cited by 1 publication

(1 citation statement)

References 33 publications

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“…Huang et al [34] proposed a DeePar framework which can exploit all the available resources from the device, the edge, and the cloud to improve the overall inference performance. Lin et al [35] proposed a cost-driven offloading strategy based on a self-adaptive particle swarm optimization (PSO) algorithm using the genetic algorithm (GA) operators (PSO-GA) to optimize the system cost during offloading DNN layers over the cloud, edge, and devices.…”

Section: Hierarchy-basedmentioning

confidence: 99%

HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing

Liu¹,

Chen²,

Zhou³

et al. 2020

Preprint

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Nowadays, deep neural networks (DNNs) are the core enablers for many emerging edge AI applications. Conventional approaches to training DNNs are generally implemented at central servers or cloud centers for centralized learning, which is typically timeconsuming and resource-demanding due to the transmission of a large amount of data samples from the device to the remote cloud. To overcome these disadvantages, we consider accelerating the learning process of DNNs on the Mobile-Edge-Cloud Computing (MECC) paradigm. In this paper, we propose HierTrain, a hierarchical edge AI learning framework, which efficiently deploys the DNN training task over the hierarchical MECC architecture. We develop a novel hybrid parallelism method, which is the key to HierTrain, to adaptively assign the DNN model layers and the data samples across the three levels of edge device, edge server and cloud center. We then formulate the problem of scheduling the DNN training tasks at both layer-granularity and sample-granularity. Solving this optimization problem enables us to achieve the minimum training time. We further implement a hardware prototype consisting of an edge device, an edge server and a cloud server, and conduct extensive experiments on it. Experimental results demonstrate that HierTrain can achieve up to 6.9× speedup compared to the cloud-based hierarchical training approach.

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Section: Hierarchy-basedmentioning

confidence: 99%