# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 2 Related Works
### Optimization of Service Migration Strategy  
Numerous studies have focused on optimizing service migration policies to ensure QoS.  
[PERSON] et al. (2020) aimed to efficiently manage the allocation of diverse heterogeneous resources and user tasks to maximize system utility in the context of vehicular edge computing. Their innovative hybrid computing offloading strategy, incorporating both vehicle-to-infrastructure and vehicle-to-vehicle communications, allows for service migration to other ESs when an ES's computing capacity is insufficient, thus achieving load balancing. [PERSON] et al. (2020) addressed mobility management across different time scales, proposing a framework that integrates service migration with transmission power adjustments. This strategy enables making service migration decisions at a broader time scale, while adjusting transmission power at a finer scale to support task offloading, aiming to minimize long-term energy consumption and ensure reliable computational offloading. Researchers in (2020) introduced a digital twin edge network architecture, distinguishing between latency-sensitive and latency-insensitive tasks. By utilizing real-time and historical data to predict future user movements, they tailored service migration decisions to reduce costs while maintaining QoS. [PERSON] et al. (2020) considered the comprehensive migration costs associated with service migration, integrating both computing and communication costs. In dynamic networks, they employed reinforcement learning combined with transfer learning to derive effective migration strategies within a dynamic vehicular edge computing environment. [PERSON] et al. (2020) tackled user mobility by modeling the service scheduling problem in an MEC environment, proposing a service management method using a probabilistic approach to effectively reduce service delay and migration costs. [PERSON] et al. (2020) emphasized the importance of balancing benefits and service costs during migration, proposing a dynamic service migration algorithm based on DRL aimed at minimizing the weighted sum of service delay and migration costs. [PERSON] et al. (2020) introduced an edge caching strategy balancing energy and latency, utilizing a deep neural network for predicting future request content, followed by determining an optimal caching placement with the branch-and-bound algorithm and refining service migration strategies using a DQN algorithm to reduce service latency. [PERSON] et al. (2020) used a DRL algorithm to optimize service migration decisions in ES, focusing on minimizing user-perceived latency and system energy consumption, addressing service interruptions caused by user mobility. Additionally, [PERSON] et al. (2020) developed a service migration optimization algorithm based on deep recursive Q-learning,aiming to minimize both user latency and system energy consumption by considering user mobility and the coverage range of ESs.  
Current research on service migration predominantly focuses on cost reduction but often overlooks the significant reduction in computational time during migration, which can adversely impact the performance of time-sensitive services. Moreover, the dynamics between service migration strategies and resource allocation decisions have not been extensively studied. Effective strategies must not only minimize data transfer delays by considering user proximity but also rigorously evaluate the computational capacities of various ESs. This ensures that the resources available post-migration are adequate to support the services without causing timeouts. Joint consideration of service migration and resource allocation is crucial for improving system performance and user experience.