# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 1 Introduction  
In recent years, edge computing has become a prominent concept in cutting-edge research and technology. Edge nodes are closer to the terminal devices of users and hence speed up data processing and transmission. This is very important for real-time applications that are highly sensitive to low latency, such as face recognition using artificial intelligence (AI) algorithms. Users can benefit from edge services by offloading their computing tasks to nearby edge servers. Thus, users obtain higher bandwidth, lower latency, and more computing power. This results in a better user quality of service (QoS) and user experience, which are important for many AI and Internet of Things (IoT) applications. Several recent studies on edge computing have focused on energy consumption, low latency, and service security to improve the QoS. [PERSON] et al. [1] studied fog calculations in an energy-consuming environment and compared them with energy consumption in cloud centers. They proved that edge computing can reduce the energy consumption of the system. [PERSON] et al. [2] studied the deployment of services in a mobile edge environment to reduce the average response time of edge servers and improve the QoS for users, while [PERSON] et al. [3] used experiments in Wi-Fi and LET networks to demonstrate that edge computing could significantly improve the response time and reduce the energy consumption of mobile devices. [PERSON] et al. [4] proposed an aggregation encryption scheme to solve the problem of data transmission security in an edge-computing environment. This scheme reduces the amount of computation and communication overhead and ensures thesecurity and effective provision of edge computing. These studies mainly aimed to improve resource utilization and reduce service latency for edge computing. However, related studies on service reliability in edge-computing environments are limited. In practice, edge computing is more vulnerable to hardware failure than traditional cloud computing owing to geographical decentralization and a lack of technical support. Device/node failure is one of the main reasons for a service shutdown [5]. Therefore, in an edge environment, it is necessary to take measures to ensure service reliability and avoid service failures caused by device downtime. In cloud computing, service reliability can be improved through fault prediction [6]. By collecting data on the status of the equipment over a long period, we can build a model based on historical data. Hence, by monitoring the real-time status of the equipment, we can forecast its future status based on an information model built using historical data. Thus, the cloud-computing environment can be monitored in real time. This approach works well in cloud-computing services, but in an edge environment, it is not very effective when applied in a straightforward manner. In contrast to cloud-computing center devices, it is not feasible to have uninterrupted human operation and maintenance for edge devices owing to their geographically scattered locations. Even if only equipment failure predictions are made, equipment downtime cannot be reduced and, therefore, service failure cannot be avoided. Therefore, an automatic fault-tolerance mechanism must be applied to ensure service reliability. With the development of container technology, the use of containers for deploying edge services has become important. A container is a relatively independent environment similar to that of a virtual machine. Containerization separates the application configuration from the underlying hardware and enables multiple applications to share the same operating system on a single physical machine, thereby significantly reducing the cost of server operation and maintenance. Owing to their lightweight nature, container technologies can be combined with edge computing. Therefore, services in edge environments are being gradually provided in the form of containers.