# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 2 Related Works
### Joint Optimization of Service Migration and Resource Allocation  
This section explores the challenges associated with optimizing service migration and resource allocation in MECC environments, a topic that has received limited attention in existing research.  
[PERSON] et al. [33] addressed user mobility in cellular networks to ensure seamless task migration between base stations without compromising resource efficiency or link reliability. Their research optimized migration and handover policies by jointly managing computational and radio resources. The policy framework integrated virtualization, I/O interference between virtual machines, and challenges associated with wireless multi-access. They developed a solution based on relaxation and rounding that includes an optimal iterative algorithm and a novel integer-recovery design. This approach surpassed traditional rounding methods by leveraging derived problem properties and applying matching theory. Additionally, their study included \"hotspot mitigation\", aiming to redistribute the load from overloaded to idle servers or base stations. Simulation results validated the effectiveness of their policies in multi-cell MECC networks, demonstrating near-optimal performance in managing joint service migration and base station handover. Building on this, [PERSON] et al. [34] proposed a method to reduce access latency for IoT users in MECC by jointly optimizing service migration and resource allocation. They introduce a Service Migration and Resource Allocation (SMRA) algorithm based on DRL, which accounts for the mobility of IoT users. This algorithm determines whether to migrate services, identifies optimal migration destinations, and allocates resources using the long short-term memory (LSTM) and the parameterized deep Q-network (PDQN) algorithms.  
Despite the recognition of the interdependencies between service migration and resource allocation, existing studies often overlook scenarios where tasks are incomplete before migration. Such oversight can result in data loss and significant deterioration in QoS. Addressing these critical aspects is essential for developing more effective migration and resource allocation strategies, ultimately enhancing both system performance and user experience.  
To underscore the novelty and uniqueness of our work, we compare our study with existing research in the field and identify several key distinctions:  
* **Acknowledgment of migration delays and impacts:** Unlike existing studies that often overlook the critical impact of the reduction in computational time caused by migration processes, our research takes these factors into account. We analyze the direct consequences of migration processes on the operational efficiency of systems, ensuring a more comprehensive understanding of the migration dynamics.
* **Exploration of service migration and resource allocation interplay:** The interaction between service migration strategies and resource allocation decisions has not been thoroughly examined in prior research. Our study delves into this interplay, aiming to establish a balanced approach that optimizes both elements to improve overall system performance.
* **Consideration of incomplete tasks during migration:** While a few studies have begun to address the interdependencies between service migration strategies and resource allocation decisions, they rarely consider scenarios where tasks are not completed before migration. This oversight can lead to significant challenges, such as the need to migrate unfinished task data and service contexts together, which can further complicate resource allocation strategies. Our work addresses this gap by incorporating these scenarios into our optimization model, aiming to minimize disruptions and enhance service quality.  
By addressing these aspects, our study not only contributes to the academic understanding of service migration dynamics but also offers practical insights that can be applied to improve the responsiveness and efficiency of IoT applications in MECC environments.