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

by   Bin Lin, et al.

Currently, deep neural networks (DNNs) have achieved a great success in various applications. Traditional deployment for DNNs in the cloud may incur a prohibitively serious delay in transferring input data from the end devices to the cloud. To address this problem, the hybrid computing environments, consisting of the cloud, edge and end devices, are adopted to offload DNN layers by combining the larger layers (more amount of data) in the cloud and the smaller layers (less amount of data) at the edge and end devices. A key issue in hybrid computing environments is how to minimize the system cost while accomplishing the offloaded layers with their deadline constraints. In this paper, a self-adaptive discrete particle swarm optimization (PSO) algorithm using the genetic algorithm (GA) operators was proposed to reduce the system cost caused by data transmission and layer execution. This approach considers the characteristics of DNNs partitioning and layers offloading over the cloud, edge and end devices. The mutation operator and crossover operator of GA were adopted to avert the premature convergence of PSO, which distinctly reduces the system cost through enhanced population diversity of PSO. The proposed offloading strategy is compared with benchmark solutions, and the results show that our strategy can effectively reduce the cost of offloading for DNN-based applications over the cloud, edge and end devices relative to the benchmarks.



There are no comments yet.


page 1


A Fuzzy Scheduling Strategy for Workflow Decision Making in Uncertain Edge-Cloud Environments

Workflow decision making is critical to performing many practical workfl...

Distributed Deep Neural Networks over the Cloud, the Edge and End Devices

We propose distributed deep neural networks (DDNNs) over distributed com...

Early-exit deep neural networks for distorted images: providing an efficient edge offloading

Edge offloading for deep neural networks (DNNs) can be adaptive to the i...

Privacy Aware Offloading of Deep Neural Networks

Deep neural networks require large amounts of resources which makes them...

A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing

Compared to traditional distributed computing environments such as grids...

ML-EXray: Visibility into ML Deployment on the Edge

Benefiting from expanding cloud infrastructure, deep neural networks (DN...

QUDOS: Quorum-Based Cloud-Edge Distributed DNNs for Security Enhanced Industry 4.0

Distributed machine learning algorithms that employ Deep Neural Networks...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.