  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.
