# Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems
## 6 Experimental Results and Analysis
### Experimental Setup
The running time of the program for each scheme is shown in Table 2. The running time of the exhaustive method greatly exceeds that of the other schemes; therefore, although the exhaustive method yields the optimal solution, the running time is unacceptable. The JGORA scheme does not consider the cloud computing processing model and has a higher running time than the other schemes. The running time of the GTPSO scheme, although higher, is maintained at the millisecond level and is negligible compared to the total time (at the second level) of edge computing task execution. Therefore, the GTPSO scheme is the more practical suboptimal solution with respect to combined performance and runtime. Figure 3 gives a comparison of the system utilities of different schemes at the termination of an iteration with a different number of users. The system utility values of all schemes increase with increases in the number of users. Among all the schemes, the GTPSO scheme has the best performance because the GTPSO scheme can fully utilize the MEC and cloud computing resources. The cloud center is responsible for the overall algorithm scheduling, and the MEC server is responsible for the endpoint task processing, which jointly optimize the computing resources and offloading strategy. \begin{table}
\begin{tabular}{c c} \hline \hline
**Scheme** & **Time** \\ \hline TOPSO & \(3.7\pm 0.2\) \\ JGORA & \(21.2\pm 2.5\) \\ ECBL & \(5.2\pm 0.7\) \\ GTPSO & \(11.3\pm 0.4\) \\ Exhaustive & \(3627\pm 36\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Runtime of schemes (ms). Figure 2: Comparison of system utility. Figure 4 gives the average task latency for each scheme at different computational loads for \(N=20\) and \(M=6\). The task processing latency increases as the computational load increases. Among all the schemes, GTPSO has the smallest latency, followed by the TOPSO scheme, because the TOPSO scheme does not consider the optimization of computational resource allocation. The JGORA scheme has the largest latency because it can only offload tasks to resource-constrained edge servers without considering the scheduling capability of cloud resources. As the computational load continues to increase, the GTPSO scheme has more significant advantages. Figure 5 gives a comparison of the system utility convergence of the GTPSO scheme with the standard particle swarm algorithm scheme (SPSO) and TOPSO scheme in the preprocessing strategy stage to evaluate the impact of the improved particle swarm algorithm on the global system utility value. With increases in the number of iterations, the user's utility value increases. The TOPSO scheme converges firstly due to ignoring resource allocation, but the system utility value is lower. The SPSO scheme does not consider the computational load caused by multiple end tasks offloaded to the same MEC server, and the system utility value is lower than that of the GTPSO scheme. The GTPSO scheme has  
Figure 4: Average task delay against computation load. Figure 3: System utility against different numbers of users. about 8% and 15% performance improvement compared to the TPSO and TOPSO schemes. Figure 6 gives the system convergence of the GTPSO scheme for \(N=20\) and \(M=6\) with different amounts of computing resources in the cloud center. The system utility value gradually increases with the number of iterations and finally converges to stability. As the amount of cloud computing resources increases, the cloud center performs the policy schedule faster, the system latency decreases and the utility value increases. Each iteration of the GTPSO scheme optimizes the two processes of computing resources and offloading policies to increase system utility until the system reaches Nash equilibrium. Figure 7 gives the system convergence of the GTPSO scheme for \(N=20\) and \(M=6\) with different amounts of edge server computing resources.