## 2 Related Work
For the multi-user random motion scenario, [PERSON] et al. [36] used the container as the target of service migration to minimize the average perceived delay of users and the average system energy, and proposed a Deep Reinforcement Learning algorithm based such a Multi-user Server Migration strategy (DRLMSM). The DRLMSM algorithm uses Software Defined Network (SDN) to arbitrarily segment traffic on any path, and allows multiple containers to be migrated simultaneously through multiple routing paths, enabling quick decision-making in unstable MEC environments. [PERSON] et al. [37] applied containers in industrial Internet edge computing, and proposed an Improved Genetic-Simulated Annealing Algorithm (IGSAA). The algorithm can solve the problems of container deployment failure and slow deployment caused by hardware failure by improving the initialization, crossover, and mutation operations of the genetic algorithm, so that the edge computing system can still provide services when the container deployment fails, and the reliability of the service is improved. Similar to [31], [PERSON] et al. [38] also use edge caching technology and proposed a cooperative edge caching strategy based on the energy delay balance to solve the problem of service interruption caused by user movement. First, the cache value model is trained by deep learning, and the content with higher potential cache value is selected for caching in the region, then the service migration problem is modeled as MDP, and finally the Deep Q-Network (DQN) algorithm to find the optimal solution of the model. Although deep reinforcement learning can adapt to complex state-space environments, it is difficult for a single super-agent to learn large-scale decentralized policies as the number of participants grows exponentially. Therefore, [PERSON] et al. [39] proposed a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, decomposes a single overall agent into multiple simpler agents to reduce the dimensionality of state space and action space, and constructs a deep q-network based on bi-branch convolution to minimize service migration cost and migration time. However, most of the above methods ignore the impact of ESs performance fluctuations in edge computing on service migration. [PERSON] et al. [40] synthesized the Primary-Backup (PB) and DQN models for developing a novel fault-tolerant service composition method. [PERSON] et al. [41] extended the traditional PB fault-tolerant model based on the idea of containers, and reduced the delay by changing the execution time of task copies to achieve reliable task services. An adaptive adjustment mechanism of container resources is also proposed, which can adaptively supplement the resources of ESs with insufficient processing capability during task execution, so as to improve the resource utilization of edge nodes. [PERSON] et al. [42] proposed a service migration strategy that can proactively predict failures (PreGAN) based on Generative Adversarial Networks (GAN) to achieve active fault tolerance in containerized edge deployments. PreGAN uses Graph Attention Networks (GAT) and Gated Recurrent Unit (GRU) for feature extraction, and a multi-head attention and a prototype prediction decoder to detect and classify faults. PreGAN utilizes a generator model to utilize anomaly class prototypes to output a delta scheduling decision to correct faults and improve QoS. [PERSON] et al. [43] considered time and cost, and the Pareto optimal method was used to select several suitable migration paths for simultaneous migration to achieve the purpose of fault tolerance. Still, the change of ESs failure rate was not considered. To avoid single points of failure and data loss, ES can replicate data to alternate hosts for fault tolerance, but replicating data at the speed that data arrives is inefficient and will consume significant network bandwidth resources. Thus, [PERSON] et al.