# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 2 Related Work
### Service Migration in Edge Computing  
Currently, several studies focusing on container migration strategies have been reported, and most of these are in mobile edge-computing environments or consider load balancing of the edge network. The purpose of a container migration strategy is to move containers from overloaded nodes to relatively healthy nodes with a high migration efficiency when container migration is triggered. In summary, a container migration strategy addresses two main issues: container selection for migration and container remapping. In [15], the author proposed a minimum transfer time selection strategy to minimize container transfer to end users. In this study, all containers for migration were selected using overloaded node sorting. The migration time was calculated as the ratio of the memory resources used by the container to the bandwidth available to the device. The container with the shortest migration time was selected to minimize the migration time. On this basis, in [16], the authors not only considered the migration time but also comprehensively considered the energy consumption of physical and virtual machines. This approach migrates the virtual machines with the lowest CPU utilization on overloaded physical machines and realizes the selection strategy of maximum migration benefit by combining the migration time and energy cost. In [17], the authors first considered the prediction of the future load of nodes and then considered the migration quantity, energy use, and communication overhead between virtual machines to achieve the optimal migration strategy in the case of multiple objectives. In [18], to avoid service quality degradation caused by resource overload on physical machines, the authors proposed a migration selection strategy for maximum CPU utilization. [PERSON] et al. [19] proposed the random container migration strategy (RCMS), which is a virtual machine selection algorithm based on a minimum migration strategy. It randomly generates uniform discrete values and then determines the devices to be migrated according to these values. In [20], a service migration mechanism based on mobile awareness was proposed to solve the problem of multinode service migration in smart contract edge computing. By optimizing business selection and finding the corresponding destination nodes, delays in business and service can be reduced, and the QoS can be improved. However, in these studies, the migration timing considered in the load-balancing solution using the container migration strategy was mostly for edge node overload. [PERSON] et al. (2023) applied LSTM and GPT2 to classify software code for potential vulnerabilities [21]. [PERSON] et al. (2023) used machine-learning algorithms on the price forecasting on e-commerce data [22]. To address this gap in the literature, in this study, the container migration timing is determined by fault prediction in the first step. Container migration is a typical non-deterministic polynomial (NP)-hardening problem. Most existing studies have used heuristic algorithms to obtain high-quality approximate solutions. [PERSON] et al. [23] proposed a dynamic virtual machine migration strategy. Their virtual machine selection strategy was based on CPU utilization. The virtual machine with the highest priority migrates to the virtual machine that can maximize the load-balance degree. The procedure was repeated until all the virtual machines selected for migration were complete. [PERSON] et al. (2017) proposed a container migration algorithm based on an improved GA [24]. The container migration problem was solved effectively using the encoding, crossover, and mutation operations of a GA. In [25], a multi-objective ant-colony algorithm was proposed, which comprehensively considered the total resource waste and power consumption required to effectively save energy. [PERSON] et al. (2021) proposed a decision mechanism and migration algorithm based on container migration [26].