## 5 Performance Evaluation  
Simulation experiments were developed on the Python platform for the DESCO network environment. The MDPSO algorithm was deployed based on this distributed environment. The service coverage area of distributed MEC nodes is a regular hexagonal region with a diagonal length of 200 m. The values of system time slots, system operating cycle, server's wireless transmission bandwidth, server's and MUs' computing power, user's channel gain \(\zeta_{n}\), and the local computing energy consumption coefficient \(\zeta\) are all constants and remain unchanged in the subsequent comparative experiments. The specific values are shown in Table 2.  
During the initialization phase, DESCO randomly generates the data for all services in the set K. The values are sampled from the intervals \(\theta_{k}\in[1,\theta_{\max}]\), \(\omega_{k}\in[1,\omega_{\max}]\), and \(\phi_{k}\in[1,\phi_{\max}]\), where the maximum values \(\phi_{\max},\omega_{\max},\theta_{\max}\) are all set to 5. The service cache capacity S of MEC server \(d\) is fixed at 2 GB, and the cache replacement policy is set to random caching by default. In the subsequent experiments, we will investigate the average energy consumption of users under different numbers of MEC servers. The environment configuration is the same as [3; 31; 33]. The state transition probabilities of user requests follow a Zipf distribution parameterized by \(\langle R,L,\vartheta\rangle\), where  
\[\Pr\{\mu_{n}^{t+1}=b|\mu_{n}^{t}=a\}= \begin{cases}R,\text{a}\
eq 0,&b=0\\ \frac{1-R}{L},a\
eq 0,&b=(a+l)\text{ mod }(K+1),l\in[1,K]\\ \frac{1-R}{b^{\vartheta}\sum_{i\in[l,K]}(\frac{1}{l})},a=0,&b\
eq 0,\end{cases}  \tag{47}\]  
whereby \(\vartheta\) represents the Zipf distribution parameter. \(L\) denotes the number of adjacent services that may be requested in the next stage. \(R\) signifies the probability that user \(n\) will not request any service in the subsequent phase. We will adjust parameters \(L\) and \(R\) to evaluate the performance of the algorithm under varying transition probabilities. (1) The Greedy-Random algorithm: combines Greedy RCO with random cache replacement strategy. In the initialization phase, MEC servers randomly generate the RCO strategies for the user. Then, based on this initial strategy, the algorithm traverses each user's offloading decision in the set \(N_{d}\). At each step, it searches for the channel occupancy strategy that minimizes the energy consumption for that user. Meanwhile, the MEC server adopts a random cache replacement strategy for local cache space. This strategy randomly replaces the stored application data with the application data offloaded by users in the current time slot after the cache space becomes saturated, satisfying Equation (18) during this process. (2) Random cache replacement with multi-dimensional discrete particle swarm offloading (MDPSO-Random): The MDPSO strategy is utilized to solve RCO strategy. By assigning initial momentum to random multi-dimensional particles, the RCO strategy explores the optimal strategy in the solution space based on the fitness function. It is worth noting that this algorithm is deployed only in DESCO networks with a single MEC server. (3) The MDPSO-Random algorithm with Cache Chord (MDPSO-Random with CC) builds upon the MDPSO-Random algorithm: This mechanism allows MUs to access the application cache resources across the entire edge layer. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of MEC servers \(\Delta\) & 3 \\ Subchannel bandwidth b & 3 \\ \(\xi\) & \(5\times 10^{-27}\) \\ \(\sigma^{2}\) & \(2\times 10^{-13}\) \\ \(\psi_{n}\) & 0.5 W \\ \(F\) & 10 \\ \(D\) & 2000 \\ \(o_{d,n}\) & 1 GHz \\ \(\theta_{\max},\omega_{\max},\phi_{\max}\) & 5 \\ \([h1_{\min},h1_{\max}]\) and \([h2_{\min},h2_{\max}]\)The effectiveness of the MDPSO algorithm and Cache Chord is verified from three aspects: convergence of the MDPSO algorithm, energy consumption performance, and cache hit rate.