# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 2 Related Work  
### Task Offloading in Edge Computing  
In recent years, the detection and prediction of anomalies and failures have aroused significant interest in the research community. These methods can be classified into detection and prediction schemes. Detection schemes identify faults at the time of failure, whereas prediction schemes attempt to predict faults before they occur. Fault prediction is used to predict the operating status of the equipment using relevant prediction methods based on current equipment information [8]. According to Tellme Networks, for system recovery in a distributed environment, 75% of the recovery time is used to detect system failures, and 18% is used to diagnose failures [9]. In general, 65% of failures can be avoided if failure detection is executed on the face [10]. Therefore, efficient and accurate detection of edge server failures is key to service reliability.  
Equipment failure prediction can be primarily classified into prediction methods based on mechanism models and data-driven prediction methods. [PERSON] et al. [11] proposed a fault detection method for cloud-computing systems based on adaptive monitoring. By monitoring various attributes of the system, a correlation analysis was conducted to establish the correlation between each measure, and key measures were selected for system monitoring. Principal component analysis (PCA) was used to analyze the data. In this manner, principal feature vectors were extracted to describe the operating state of the system and hence establish a reliability model for the system that can be used to predict system failure.  
For some devices and systems, owing to their complex internal structures or model parameters, it is impossible to directly use mathematical or physical methods to build a mechanism model. Data-driven fault-prediction methods have been developed for such cases. In data-driven fault prediction, the future state performance of the equipment is predicted using historical data on the operating status of the equipment and relevant models to obtain the final fault-prediction results effectively and efficiently [12]. In recent years, with the continuous development of machine-learning technology, an increasing number of machine-learning algorithms have been used in data-driven prediction methods because they can establish the correlation between data and failure states without accurate mechanism models in advance.  
Data-driven fault-prediction technology is based on real data and extracts the implied information for prediction through data analysis and processing methods. This has become a practical fault-prediction technology. In [13], an online detection model called a support vector machine (SVM) grid, which is based on an SVM, was used to predict the cloud status. This approach applies PCA to select monitoring parameters and reduce dimensionality. It also optimizes the SVM parameters using a grid network. Thus, the faults in a cloud system can be detected and predicted efficiently.  
In [14], a deep neural network (DNN) based on Gaussian Bernoulli restricted Boltzmann machines (GBRBM) was proposed for IoT devices under the Industrial Internet of Things. The algorithm transforms the fault detection problem into a classification problem to achieve accurate fault detection.  
Although there are many studies on fault prediction in cloud environments, few have focused on fault prediction in edge-computing environments, where modeling and fault prediction must be performed according to the characteristics of the edge environment.