# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 5 Performance Evaluation
### Experimental Results and Evaluation Analysis
#### 5.2.2 Container Migration Evaluation  
We evaluated the container migration algorithm from two aspects: cost and degree of load balance. Figure 4 compares container migration costs under the three migration selection strategies. The cost is defined in Section 3.2. We fixed the total number of devices in the edge network at 30, and the number of devices predicted to fail gradually increased from 1 to 10. When the number of failures increases, the migration cost of the entire migration process correspondingly increases. This is because a suitable healthy node must be identified for the container in each failed device. Simultaneously, it can be clearly observed from the figure that the GA-based GACMS container migration strategy adopted in this study consumes less container migration cost than the GCMS and RCMS migration strategies.  
Figure 5 explores the load-balancing degree of the edge network when different numbers of edge devices fail in the edge network. While exploring the results of this experiment, we maintained a constant number of 30 devices in the edge network. The \(x\)-axis represents the number of predicted faulty devices, ranging from 1 to 10. The \(y\)-axis represents the load-balancing degree of the entire edge network after the container on the faulty device migrates to the surrounding healthy nodes. As shown in the figure, regardless of the number of faulty devices in the current edge network, GACMS exhibited the best degree of load balancing. Compared with the RCMS and GCMS, the proposed  
Figure 4: Container migration costs for different numbers of failed devices.  
Figure 3: Failure prediction accuracy and recall of two time-series prediction models.  
GACMS can maximize the load balance of the entire edge network after container migration. Figures 4 and 5 show that GACMS outperforms the previous methods in terms of both migration cost and degree of load balancing.  
Figure 6 shows the relationship between the total number of devices in the edge network and migration cost. In this experiment, we maintained the number of faulty devices at 10 and then changed the total number of devices in the edge network to 30, 40, and 50. The results show that the GACMS migration strategy adopted in this study has the lowest migration cost for the entire migration process compared with the RCMS and GCMS migration strategies. In other words, the proposed migration strategy under different scales of edge network equipment can achieve good results and effectively reduce the migration cost of container migration.  
Figure 7 shows the relationship between the number of devices in the edge network and the degree of load balancing. When the number of devices in the edge network changes, the number of devices predicted to be faulty remains unchanged at 10. Therefore, as the total number of devices in the network increases, more resources become available in the edge network. When the value of the load-balance degree is lower, the load of the entire edge network is more balanced. The GACMS algorithm had a lower value for all three scales: 30, 40, and 50. This clearly shows that under different network scales, the container migration strategy based on the proposed GAMCS algorithm can better ensure the overall  
Figure 5: Degree of load balancing with respect to different numbers of edge-device failures.  
Figure 6: Migration costs for different numbers of edge network devices.  
load balance and migration cost of the edge network and, hence, can effectively improve the reliability of edge network services.