## 5 Performance Evaluation
### Baseline Algorithms and Metrics  
The proposed algorithm and four comparison algorithms are as follows.  
1. DRPS: It is the algorithm proposed in this paper. A novel fault-tolerant approach for redundant-path-enabled service migration in mobile edge computing.
2. DRPS/F: It is a simplified version of the DRPS approach, where the failure rate of ESs is constant.
3. Greedy-SP: It is a traditional greedy algorithm, which first finds the current closest path and performs service migration based on the resubmission strategy.
4. Greedy-CP: It is a traditional greedy algorithm, which first find ES with the most remaining computing force within the current coverage range and performs service migration based on the resubmission strategy.
5. PLP [43]: It is based on weak Pareto optimization and selects multiple paths for service migration.  
The migration paths of the DRPS approach and the other four comparison algorithms are illustrated in Figure 4. The movement paths of edge users (vehicles) are represented by red dotted lines, and the migration paths of services are represented by solid lines. As can be observed:  
* As illustrated in Figure 4a, for independent tasks, the migration paths of DRPS and DRPS/F approach are the same, because both algorithms choose the migration path according to the failure rate of ES. However, for the task flow that is continuously reached within a period of time, because the DRPS approach adjusts the failure rate of ES dynamically, and the failure rate of the DRPS approach will be lower than that of DRPS/F, which has also been confirmed in subsequent experiments.
* As illustrated in Figure 4b, PLP algorithm always selects the path with the least time and consumption for migration, but it may increase migration time and energy consumption due to the ES failure.  
Figure 3: Pudong Central Business District (The blue dots in (**a**) represent base stations; Black squares in (**b**) indicate base stations, blue dots indicate network routers, and red lines indicate that devices can communicate with each other).  
* As illustrated in Figure 4c, the Greedy-SP algorithm selects the closest ES or NR for service migration, but it is easy to fall into the local high-quality solution and cannot find the high-quality migration path.
* As illustrated in Figure 4d, similar to Greedy-SP, Greedy-CP is also based on greedy thinking, but it always prefers the ES with the highest remaining computing power within the scope for service migration.  
This section uses the following three indicators to evaluate the algorithm's performance.  
1. Average Completion Time (ACT): Time to migrate services from source ES to target ES, including data transfer time and task computation time.
2. Task Completion Rate (TCR): The success rate of service migration. If the service is migrated to the targeted ES within the user's maximum tolerable time, the service is successfully migrated.
3. Average Failure Count (AFC): The number of service failures during migration. The more failures, the higher the cost of the service migration.  
It should be noted that because DRPS and DRPS/F choose multiple paths for service migration simultaneously, DRPS and DRPS/F are subdivided into DRPS/OP, DRPS/AP, DRPS/F/OP, and DRPS/F/AP in the experiment to evaluate the average failed times of service migration. DRPS/AP and DRPS/F/AP indicate the average number of migration failures in all migration paths, and DRPS/OP and DRPS/F/OP indicate the average number of migration failures in successful migration paths.  
Figure 4: Service migration path in VEC environment (black squares indicate base stations, blue dots indicate network routers, dotted lines indicate vehicle movement directions, and solid lines indicate task migration paths.) (**a**) DRPS&DRPS/F; (**b**) PLP; (**c**) Greedy-SP; (**d**) Greedy-CP.