## 6 Conclusions  
In this paper, an efficient algorithm named MRL-SFCP is proposed to address the SFC placement problem posed by IoT-SRs in dynamic IoT-MEC networks. The SFC placement problem is first decomposed two subproblems--VNF placement and traffic routing--and is then modeled as an MDP involving the environmental state, action space, and reward function. To capture real-time network fluctuations, two types of DDQN networks--an VNF placing network and an SFC path finding network--are specifically designed to facilitate  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Number of IoT-SRs** &
\begin{tabular}{c} **Accepted Traffic Throughput** \\ **(Gbps)** \\ \end{tabular} \\ \hline \multirow{3}{*}{MRL-SFCP} & 100 & 3.0 \\ \cline{2-3}  & 150 & 8.0 \\ \cline{2-3}  & 200 & 10.0 \\ \hline \multirow{3}{*}{DDQN-SP} & 100 & 3.0 \\ \cline{2-3}  & 150 & 7.0 \\ \cline{2-3}  & 200 & 9.0 \\ \hline \multirow{3}{*}{DLA-DVPRP} & 100 & 3.0 \\ \cline{2-3}  & 150 & 6.4 \\ \cline{2-3}  & 200 & 8.0 \\ \hline \multirow{3}{*}{DLG} & 100 & 3.0 \\ \cline{2-3}  & 150 & 6.0 \\ \cline{2-3}  & 200 & 6.2 \\ \hline \multirow{3}{*}{Random} & 100 & 3.0 \\ \cline{2-3}  & 150 & 4.3 \\ \cline{1-1} \cline{2-3}  & 200 & 4.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The accepted traffic throughput of IoT-SRs in the SFN.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Running Time per 200 SRs (ms)** \\ \hline MRL-SFCP & 200 \\ \hline DDQN-SP & 350 \\ \hline DLA-DVPRP & 660 \\ \hline DLG & 930 \\ \hline Random & 1230 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Running times of different schemes.  
optimal selection of VNF instances and determination of routing paths. We then propose a meta reinforcement learning-based SFC placement (MRL-SFCP) policy that integrates meta reinforcement learning and fuzzy logic to address the dynamic SFC placement problem. According to the simulation results, the proposed method demonstrated remarkable performance superiority according to traffic throughput, acceptance rate, and average reward of IoT-SFCRs in comparison to other baseline methods across three representative network topologies. In this paper, we assume that each IoT-SR only passes through three types of VNFs before reaching the destination node. There are certain limitations, and future research will further improve the proposed algorithm to adapt to any length of SR, enhance the capabilities of the agent, and expand the scalability and adaptability of MRL-SFCP to accommodate networks of varying scales.  
Conceptualization, S.G. and Y.D.; methodology, S.G. and Y.D.; software,Y.D.; validation, S.G. and Y.D.; formal analysis, S.G.; investigation, S.G.; resources, S.G.; data curation, Y.D.; writing--original draft preparation, S.G. and Y.D.; writing--review and editing. Y.D. and L.L.; visualization, L.L.; supervision, S.G. and L.L.; project administration, S.G.; funding acquisition, S.G. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Research Youth Project of Chongqing Municipal Education Commission under Grant KJQN202202401, Chongqing University of Posts and Telecommunications Doctoral Initiation Foundation (A2023007).  
Not applicable.  
The authors declare no conflict of interest.