## 6 Conclusions  
This paper establishes a dynamic DNN partitioning and deployment system model to represent the actual application requirements of distributed DNN inferencing in an edge environment. On this basis, the problem of optimal deployment-oriented DNN partitioning is modeled as a constrained optimization problem. Considering that the crossover and mutation operators in a basic genetic algorithm may produce many infeasible solutions, it aims to distinguish two types of chromosomes, i.e., partitioning chromosomes and deployment chromosomes. Then, it performs the crossover and mutation operations on partitioning chromosomes to ensure generating reasonable deployment chromosomes and produce new deployment chromosomes based on the updated partitioning population and the select excellent deployment individuals for the next iteration. The experimental results show that the proposed algorithm can not only result in shorter inferencing time and lower device average energy cost, but also needs less time to achieve an optimal deployment.  
To further improve this work, a potential future research direction is to try to reduce working on CPU by constructing proper mathematical models. In addition, 3D image-related applications will be considered in the future.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**DNN Model**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Exhaustive Method**} & \multirow{2}{*}{**Improved GA**} & \multirow{2}{*}{**Basic GA**} \\  & **Partitions** & & & \\ \hline \multirow{4}{*}{AlexNet} & 3 & 490.43 & **267.20** & 275.46 \\  & 5 & 7870.74 & **6563.56** & 7214.8 \\  & 7 & 3,675,603.00 & **6563.56** & 24,991.9 \\  & 3 & 490.97 & **372.15** & 381.81 \\ \multirow{4}{*}{SqueezeNet} & 5 & 41,385.8 & **6568.28** & 6713.38 \\  & 6 & 1,476,833 & **11,197.10** & 12,572.80 \\ \multirow{4}{*}{MobileNet} & 2 & 485.28 & **231.70** & 268.69 \\  & 3 & 751.76 & **335.14** & 341.12 \\ \multirow{4}{*}{ResNet110} & 4 & 29,311.00 & **1238.22** & 1357.69 \\  & 2 & 530.85 & **269.12** & 381.98 \\ \multirow{4}{*}{ResNet110} & 3 & 12,665.30 & **1620.24** & 4086.40 \\  & 4 & 5,217,793.75 & **7322.81** & 19,345.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison on algorithm running-time (ms) under different scenarios.  
**Author Contributions:** Conceptualization, J.N. and B.Z.; methodology, J.N., H.Z. and J.L.; validation, J.L.; investigation, J.N. and H.Z.; data curation, H.Z. and J.L.; writing--original draft preparation, J.N.; writing--review and editing, J.N.; supervision, B.Z.; funding acquisition, B.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Key Project of the National Natural Science Foundation of China: U1908212.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets can be obtained from [[http://www.cs.toronto.edu/~kriz/cifar.html](http://www.cs.toronto.edu/~kriz/cifar.html)]([http://www.cs.toronto.edu/~kriz/cifar.html](http://www.cs.toronto.edu/~kriz/cifar.html)) (accessed on 17 November 2021).  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.