# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 6 Contributions and Future Work  
In this paper, we propose a solution to service failure problems in an edge-computing environment. The contributions of this study are three-fold. First, we propose a novel two-phase method, the combination of fault prediction and container migration, on the edge computing. The fault prediction is used to decide when to trigger the container migration. The migration is to prevent the service failure. Second, this model can predict the possible downtime of equipment in advance through fault prediction. Third, we can model the container migration problem on the faulty device as an optimization problem and apply a reasonable container migration strategy in which containers on faulty devices can be migrated to other healthy devices to ensure service reliability. Our experiments show that this solution can overcome the service failure problem in edge-computing environments. The main research content of this study is summarized below. Considering the prediction phase, it is difficult to predict the failure of edge devices. To achieve this, it is necessary to develop a fault-prediction algorithm that exhibits high accuracy. Therefore, we selected two commonly used time-series prediction algorithms: LSTM and GRU. By comparing the accuracy and recall rates of the two algorithms and the training loss function, we chose the GRU model with the best performance to predict the failure of edge devices. This approach achieves an accuracy of 0.94. Thus, our approach can significantly reduce false-positive container migration and hence reduce unnecessary waste of resources. Considering the migration phase, if it is predicted that the device will fail, we must migrate the container deployed on the device. At present, container live migration technology can maintain the state of service, which can be used without the user's perception. However, if the migration is performed without considering the migration strategy, the number of containers migrating to a healthy device may be too large, and the device may be overloaded, which not only reduces the QoS but even causes the device to fail. Failure is the final downtime; therefore, a reasonable migration strategy must be considered. This study proposes a container migration strategy based on a GA that minimizes the migration cost and ensures the overall load balance of the network, thus effectively ensuring the service reliability of edge devices. Finally, by combining the two methods of fault prediction and container migration, the service failure problem caused by the downtime of edge devices can be solved, the load balance of the edge network can be ensured, the migration cost of container migration can be reduced, and the entire edge network can achieve better reliability. Figure 7: Degree of load balancing with respect to the size of the edge network. In the future, we would like to continue to explore this problem via the following two aspects:  
(1) The fault-prediction algorithm used in this article requires historical fault data for support. However, in many cases, historical fault data may not exist. Therefore, in the future, we will develop a method that does not require historical fault data and characterize the normal status of a system via training with normal data. Then, we will perform fault prediction using the developed model. (2) Regarding the migration strategy, it is easy to fall into local optimality when using GAs, and the training time is relatively long. In the future, we will adjust the GA to avoid this issue. We will also explore the use of other models to overcome this problem. Moreover, only load balancing and migration costs are currently considered, and additional factors must be considered considering the complexity of real-world scenarios. Conceptualization, L.L. and Z.Z. ; methodology, Z.Z. ; software, L.L. ; validation, L.L. and Z.Z. ; formal analysis, L.L. ; investigation, L.K. ; resources, Z.Z. ; data curation, Z.Z. ; writing--original draft preparation, L.L. ; writing--review and editing, X.L. ; visualization, Z.Z.