# Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems
## 6 Experimental Results and Analysis  
### Experimental Setup  
To verify the performance of the proposed task offloading scheme, we performed experimental simulations on a Windows PC using Matlab2021 data analysis and Python programming software. We considered face recognition or body temperature monitoring as the user's computational task and evaluated the overhead of task offloading for the end-user based on the utility value of the task processing system. Since there is no standardized experimental platform or data for edge computing, we used self-combining data for our experiments. Without a loss of generality, we used a centralized cloud server with an MEC collaboration scheme under a fiber-radio hybrid network. The communication and computational parameters in this paper were set concerning the literature [32; 33], and the uplink channel gain was generated by the path loss model \(L(dB)=140.7+36.7\text{loglog}_{10}\,d_{[km]}\). The standard deviation of the log-normal shading was set to 8 dB. Unless otherwise stated, we chose the input data amount from [200-600] KB randomly, the required CPU cycle was 1 GHz and the time and energy weighting factors were set to \(\beta_{n}^{t}=0.2\) and \(\beta_{n}^{\sigma}=0.8\), respectively. Other partial parameters were set as shown in Table 1.  
The following scenarios were used as comparison scenarios for GTPSO:  
1. Exhaustive: This is a brute-force method that finds the optimal offloading scheduling solution via an exhaustive search of over \(2^{n}\) possible decisions. Since the computational complexity of this method is very high, its performance is only evaluated in a small network setting. 2. Task offloading by the particle swarm optimization algorithm (TOPSO): Using the particle swarm optimization algorithm in [34] for task offloading and introducing the cloud center for offloading scheduling, the TOPSO scheme does not consider the resource allocation scheme. 3. Joint Greedy Offloading and Resource Allocation (JGORA): All tasks are offloaded [35], and each offloaded user greedily selects the subchannel with the highest channel gain until all users are admitted or until all subchannels are occupied. The JGORA scheme does not account for the cloud computing processing model. 4. ECBL: The literature [20] proposes an improved artificial bee colony algorithm to find the optimal allocation scheme. The ECBL scheme considers the cloud-side collaborative system but does not consider the task local processing scheme. Performance Evaluation  
To verify the suboptimality of the algorithm, GTPSO was compared with other schemes, and the system utility values of all schemes at different loads for \(N=10\) and \(M=4\) are given in Figure 2. We found that the exhaustive method has the highest utility value, and the GTPSO scheme has significantly better utility values than the other schemes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Parameter** & **Numerical Value** \\ \hline Cloud center CPU frequency & 20 GHz \\ Edge server CPU frequency & 10 GHz \\ User CPU frequency & 1 GHz \\ Cloud transmission rate \(r_{c}\) & 1 us/bit \\ System bandwidth B & 20 MHz \\ Uplink power \(p\) & 20 dBm \\ Channel gain \(h\) & 0.6–0.8 \\ Noise power \(\sigma^{2}\) & \(-100\) dBm \\ \(\beta_{n}^{t}\) & \(10^{-9}\) cycle/J \\ \(\theta_{n}^{m}\) & \(10^{-10}\) cycle/J \\ Penalty coefficient \(g\) & \(10^{-2}\) \\ Inertia weight \(w_{max}\) & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental parameters. In addition, with increases in computational load, the computation required for the task increases, which causes greater latency and energy consumption, and the systematic utility value decreases overall. There was a performance improvement of 15%, 10% and 7.1% compared to the TOPSO, JGORA and ECBL schemes, respectively.