# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 2 Related Works
### Joint Optimization of Dynamic Computation Offloading and Resource Allocation  
This section reviews existing research on strategies for task offloading and resource management in dynamic network environments. [PERSON] et al. [13] considered the mobility characteristics of user equipment (UE) and proposed a dual time-scale framework that resolves user-server association problems by incorporating long-term channel interference, workload, and server computational constraints with short-term dynamic task offloading and resource allocation. [PERSON] et al. [14] developed a decentralized offloading framework, accommodating mobile users dynamically entering or exiting an MEC system, and adapting to their varying offloading demands. [PERSON] et al. [15] introduced a priority-driven multi-agent (PDMA) cooperative task offloading algorithm to address the dynamic characteristics of task arrivals, mobility of devices, and load imbalances across ESs. [PERSON] et al. [16] investigated joint task cache placement and offloading in mobile edge computing systems characterized by dynamic task arrivals. [PERSON] et al. [17] devised a dynamic task offloading algorithm using DRL, considering dependency relationships among user tasks to optimize task offloading and resource allocation decisions effectively. This algorithm seeks to minimize task completion time and reduce device energy consumption amid channel variations. [PERSON] et al. [18] proposed a scheduling algorithm that integrates communication and dynamic tasks by considering the vehicle's mobility patterns and task sizes in vehicular networks supported by an intelligent reflecting surface (IRS) for MEC services. [PERSON] et al. [19] based their joint offloading strategy on vehicle mobility patterns, aiming to minimize the weighted sum of execution time and computation costs, considering both response delay and economic factors. [PERSON] et al. [20] introduced a task offloading cost model for scenarios involving multiple vehicles and MEC servers, utilizing the DDPG algorithm to make decisions that minimized the overall system's task processing costs. [PERSON] et al. [21] explored task execution queues and priorities within a multi-MEC server environment, dividing computation offloading into power scheduling and task offloading phases. In the power scheduling phase, the focus is on minimizing energy consumption through optimal transmission power and CPU frequency settings, while the task offloading phase aims to reduce execution latency through strategic offloading decisions. [PERSON] et al. (2020) addressed a dynamic Internet of Vehicles (IoV) architecture that supports both MEC and cloud computing, employing a DRL-based algorithm to optimize task offloading and resource allocation for self-driving vehicles on straight roads. This algorithm aims to minimize processing costs by optimizing computational offloading and bandwidth allocation, adhering to processing delay and transmission rate constraints. [PERSON] et al. (2020) proposed a vehicular edge computing architecture utilizing non-orthogonal multiple access (NOMA), focusing on cooperative resource optimization among ESs to maximize the service ratio through game theory and convex optimization methods. While recent advancements in dynamic computational offloading and resource allocation have significantly mitigated issues related to user mobility by optimizing offloading and allocation decisions to minimize response times, there remains a notable gap when it comes to fully addressing the dependencies of task execution on the underlying environments and user contexts at ESs. In scenarios where user mobility is rapid, optimizing task offloading alone proves insufficient. This is due to the fact that task execution often depends on specific characteristics of the ESs and user contexts. Consequently, integrating service migration strategies that dynamically adapt to these environmental and contextual dependencies is crucial.