## 6 Conclusions  
This paper investigates how to provide higher-quality services to MUs while reducing user layer energy consumption in MEC networks. The DESCO network architecture is proposed to address this issue. In the DESCO network, a cache-sharing mechanism, Cache Chord, is conducted based on a geo-distributed edge server collaboration. Cache Chord utilizes consistent hashing to map servers and real-time applications into a circular logical topology. We also improved the IP routing algorithm to reduce computational complexity and designed an index update mechanism to ensure information synchronization among edge nodes. Furthermore, in addressing the bottleneck of channel allocation caused by concurrent requests from current users, the problem is formulated as a multi-player static game. After proving the existence of the Nash equilibrium point, the MDPSO algorithm is proposed to simulate user requests as particles, exploring the global optimal node in the solution space to provide users with the lowest energy consumption offloading strategy. Finally, the algorithm is compared with baseline algorithms, and the performance of the Cache Chord structure is tested at different scales.  
Through simulation experiments, we have found that computation offloading assisted by the Cache Chord mechanism can significantly reduce user energy consumption overhead. It is foreseeable that with the increasing scale of added server nodes, energy consumption will be further reduced. However, this paper overlooks the fact that with the increase in server nodes, the transmission delay of the corresponding backhaul links will also increase, posing new challenges for performance optimization. Therefore, the next step will be to explore how larger node scales will affect service quality and consider deploying the protocol in a real network environment to test its performance.  
Conceptualization, W.L.; Data curation, W.L.; Formal analysis, Y.L., W.L. and S.L.; Funding acquisition, Z.Y.; Investigation, P.Z.; Methodology, Y.L., P.Z., Z.Y. and W.L.; Project administration, Y.L. and Z.Y.; Resources, S.L.; Software, P.Z. and S.L.; Validation, Y.L.; Visualization, Z.Y.; Writing--original draft, P.Z.; Writing--review and editing, Y.L. and P.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Science and Technology Project of Hebei Education Department ZD2022102.  
The data can be shared upon request and are not publicly available due to the data also forms a part of an ongoing study.  
The authors gratefully appreciate the anonymous reviewers for their valuable comments.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MEC & Multi-Access Edge Computing \\ WAP & Wireless Access Point \\ RSU & Road Side Unit \\ TDMA & Time-Division Multiple Access \\ DESCO & Distributed Edge Service Caching and Offloading \\ RCO & Real-Time Computation Offloading \\ NE & Nash Equilibrium \\ CC & Cache Chord \\ MUs & Mobile Users \\ DHT & Distributed Hash Table \\ P2P & Peer to Peer \\ DVFS & Dynamic Voltage and Frequency Scaling \\ FLOPs & Floating-Point of Operations \\ FLOPS & Floating-Point Operations Per Second \\ MDPSO & Multi-Dimensional Discrete Particle Swarm Optimization \\ \end{tabular}