## 5 Performance Evaluation
Firstly, the convergence analysis of the proposed algorithm is conducted. Figure 4 illustrates the instantaneous energy consumption obtained after iterations for both the MDPSO algorithm and the Greedy algorithm in the single-server scenario. The experiment is conducted with different MU counts (\(N_{d}=15,25,35,45\)) and a local MEC cache capacity of 2 GB. The horizontal axis represents the number of iterations for particles or algorithms, while the vertical axis represents the energy consumption of the MEC server. In the experiment, the dimension \(L\) of particles is set to 500, and the number of iterations \(i\) is 100. The blue line represents the convergence curve of the MDPSO, while the orange line represents the Greedy algorithm. The green horizontal line, which remains constant throughout iterations, represents the energy consumption of local computation. Comparing the two algorithms, it is observed that both MDPSO and Greedy strategies converge within the first 40 rounds and can reduce user-level energy consumption by 18.75% to 41.17% compared to local computation. Moreover, MDPSO exhibits better convergence compared to the Greedy strategy, with an average reduction of 19.7% in user-level energy consumption per round. This indicates that the MDPSO algorithm can explore the solution space more comprehensively, avoiding local optima, and performs well when dealing with discrete vectors. Figure 5 illustrates the average task completion time of user layer per time slot with a task latency constraint of \(\tau=20\) ms. The red line represents the MDPSO algorithm with collaboration among five nodes, while the green line represents the Greedy offloading strategy with the same number of collaborating nodes. Since MUs can adopt DVFS to adjust the computational power of local devices and ensure timely task completion, the task completion time in each round remains stable at a maximum latency constraint of 20 ms. After running for 1000 time slots, the proposed algorithm outperforms others, with an average latency of 7.4 ms, while the Greedy strategy exhibits an average latency of  
Figure 4: User layer real-time energy cost under different algorithms and different numbers of MUs: (**a**) MU number = 15. (**b**) MU number = 25. (**c**) MU number = 35. (**d**) MU number = 45. 11.4 ms, both showing a decrease compared to the local computing. This implies that the proposed algorithm can converge to the optimal offloading strategy, thereby applying the cache-based offloading mode as much as possible, to reduce data transmission overhead for MUs. Figure 6 compares the energy consumption of local computation, Greedy-Random, MDPSO-Random, and MDPSO-Random with Cache Chord (CC)-assisted algorithms under different numbers of MUs and task richness conditions, using the default Zipf service request distribution function. In Figure 6a, the number of users covered by each MEC server is fixed at 5. Through the experiments, it is observed that, relative to local computation, the other three algorithms effectively reduce energy consumption overhead. In Figure 6a, the curve for local energy consumption remains relatively constant. This is because the energy consumption of local computation is only dependent on the number of tasks and FLOPs of tasks. The increase in the number of user types only poses challenges for cache-based algorithms. Moreover, through horizontal comparison, it is noted that the algorithm based on the cooperation of 5 distributed MEC servers with cache exhibits the best performance. This is attributed to the CC data-sharing mechanism, which allows users at any location to access resources across the entire MEC edge layer. This implies that the cache space linearly increases with the addition of servers in the CC network, thereby eliminating the need for users to transmit \(\theta_{k}\) and resulting in a higher transmission of energy consumption. Figure 5: The comparison of average user layer latency per time slot. Models include MDPSO (server = 5), Greedy (server = 5), and local computing.