## 5 Performance Evaluation
Figure 6: User layer overall energy cost comparison. (**a**) Task number comparison. (**b**) User number comparison. Figure 7 compares the average energy consumption of a cluster of users under the collaboration of different numbers of MEC servers. As the number of MEC servers increases, the energy consumption in all cases shows a decrease and converges to the same value in all cases, because as the total number of server \(D\) increases, the cache pool storage is richer, which allows more requests to be converted from pure offloading to cache-based offloading until all MUs do not need to upload application data \(\omega_{k}\). It can also be noticed that the slopes of the three energy consumption curves decrease gradually as the total number of tasks increases. The energy consumption curve decreases most rapidly when the total number of tasks is small (\(N_{d}=100\)) and converges at around 20 servers; the energy consumption curve of \(N_{d}=100\) converges at \(D=45\) because when the total number of tasks is larger, the servers also need to collaborate on a larger scale to make sure that the corresponding application data are stored. Figure 8 illustrates the average energy consumption of MUs per time slot under different Zipf parameters and various task attributes for MDPSO. As depicted in Figure 8a, it can be observed that with the increase in the maximum value of application data \(\omega_{max}\), the average energy consumption of the Greedy algorithm and the MDPSO assisted by CC continues to rise. This is due to the increase in \(\omega_{max}\), which increases the transmission overhead of tasks, thereby reducing the offloading benefits. The proposed algorithm can effectively reduce the average energy consumption by 10.9% to 25.17% compared to the Greedy offloading strategy. Additionally, under the same parameter conditions (\(L=3\)), MDPSO based on multi-server collaboration can reduce the average energy consumption by 7.89% to 4.7% compared to the single-node mode. Observing Figure 8b, we can conclude that with the growth of \(\theta_{max}\), the average energy consumption of all algorithms increases. Moreover, comparing the Greedy algorithm with the MDPSO algorithm based on multi-node collaboration, under the same Zipf \(R\) condition, MDPSO can reduce the average energy consumption by 2.1% to 16.8% compared to Greedy. Furthermore, with the increase in parameter \(R\), the energy consumption also decreases. This is because parameter \(R\) is positively correlated with the probability that the MUs' next stage service request is empty. A larger R means more MUs with empty requests at any time. Sparse service requests undoubtedly lead to reduced transmission energy consumption for MUs. Figure 7: The comparison of average energy consumption under different numbers of MEC servers, where task number = 100, 200, 300. Figure 9a,b, respectively, explore the impact of the number of servers in the CC mechanism on the cache hit ratio from the perspectives of user count and service types. The cache hit ratio represents the ratio of the total number of accesses to the edge layer by users \(C_{s}\) to the number of services successfully requested \(C_{h}\), expressed as the following formula:  
\[\text{Cache hit ratio}=\frac{C_{h}}{C_{s}} \tag{48}\]  
By vertically comparing Figure 9, it is evident that under the same conditions, as the number of MEC servers increases, the cache space grows linearly, leading to a significant improvement in the cache hit ratio \(C_{r}\). Compared to a single server, when the number of servers is increased to 10, the cache hit ratio \(C_{r}\) increases by 33.3% to 42.7%. Moreover, when the number of users \(N_{d}\) is 20 and the number of services \(K<80\), the cache hit ratio achieved by the cooperation of 10 servers is consistently above 91%. This improvement is attributed to the Cache Chord mechanism overcoming the bottleneck imposed by the storage space limitation. However, as the number of task types increases, the cache hit ratio decreases.