# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
## 2 Related Works
### Edge-Computing Task Offloading Based on Resource Prediction  
With the large number of local devices accessed in IoT, how to improve the speed of making offloading decisions while guaranteeing the efficiency of user response has become the focus of current researchers. The resource-prediction-based offloading of edge computing tasks can effectively improve the problem of large differences in task execution results due to the diversity of temporal dependencies within the task, while the effective prediction of the computational resources required for subsequent tasks can effectively improve the efficiency of users in formulating offloading strategies. This offloading method based on resource prediction can effectively alleviate the global resource tension and realize the rational allocation of resources. The authors in [27] targeted a multi-objective task offloading scheme for edge devices that aims to optimize task execution time, device energy consumption, and rental cost, while taking into account the real-time environment of the device and the user's preferences to improve the task completion rate and reduce the associated metrics. The authors in [28] proposed a lightweight mobile prediction and offloading framework using artificial neural networks to reduce the complexity of predicting user location for computationally intensive task offloading. The authors in [29] designed a distributed service offloading D-SOAC method based on deep learning and deep reinforcement learning, which predicted the potential customer demand in the central cloud through deep spatio-temporal residual networks and collaborated with edge servers to solve the problem of how to optimize the service offloading location to reduce the user's service latency under the condition of limited computational resources, which effectively reduces the average service latency of the users. The authors in [30] proposed a cloud-edge-client federated learning task prediction mechanism based on bidirectional long- and short-term memories, in which participants only use local training models without uploading data to servers. This algorithm avoids solving complex optimization problems while ensuring user privacy and security. The authors of [31] considered the problem of conflict between resource consumption and quality of service and rationally allocate computational resources through a resource ranking method so as to reduce the probability of task failure. The authors of [32] designed an intelligent collaborative framework scheme to realize dynamic service prediction and multi-task offloading decisions. This scheme enables better task sensing, anomaly inference, and task offloading. The task prediction offloading research has a significant effect on coping with the complexity of the offloading environment caused by the diversity of user behaviors, which is helpful in alleviating the environmental overload phenomenon and improving the problem of long system-response delays. However, given that task forecasting is closely related to resource allocation, the reservation of compute resources should be fully considered when making task forecasting. The authors in [33] proposed a delay-aware resource reservation strategy based on spatio-temporal and spatial-task decentralization demand forecasting and proposed a regional-edge server resource reservation algorithm based on the prediction model to minimize the latency of terminal tasks. The authors in [34] updated the upload strategy based on feedback from the environment and optimized resource allocation with the help of Salp's artificial bee colony algorithm to improve the performance of IoT devices. The authors of [35] discussed offline decision-making and resource allocation in MEC systems, especially optimization for time-varying wireless fading channels and edge computing capabilities.