# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
## 1 Introduction  
With the popularization of the Internet of Things (IoT), a large number of computation-intensive applications are emerging, such as face recognition [1] and autonomous driving [2], which increase the pressure on terrestrial networks to transmit and process data. In addition, realistic scenarios such computation-intensive applications often have timing dependencies, which make the network environment more complex [3]. In order to effectively deal with such computation-intensive tasks with timing dependencies, mobile edge computing (MEC) has emerged. Compared with traditional cloud computing [4], MEC has the advantages of low latency, high bandwidth, and agile deployment due to being closer to users. MEC has become an effective tool to improve the processing efficiency of timing-dependent tasks in the network [5]. However, in multi-user concurrent scenarios, MEC technology still faces the constraints of edge server resource limitations and the complexity of inter-task timing dependencies while improving the user response rate. With the generation of a large number of computation-intensive tasks in time and space, the random scheduling of tasks will lead to the localized clustering of tasks on the edge servers, further inducing load imbalance of MEC network nodes [6]. This phenomenon of chaotic task processing order or server paralysis due to excessive access makes it more difficult to ensure the delay intask offloading and the timeliness of the offloading policy [7; 8]. Therefore, in realistic IoT scenarios in which multiple users concurrently apply for services, it is of great significance to formulate a node cooperative offloading strategy that can reduce the task processing delay as well as improve the resource utilization rate to enhance the timeliness of the task offloading strategy. According to the timing dependencies between subtasks and the dynamic scheduling of node resources, realizing serial and parallel computation between subtasks is the key to developing node-cooperative offloading strategies. Due to the differences in timing dependencies between the subtasks in each application and the unpredictable and dynamic change of resources on the node due to simultaneous offloading of tasks by multiple users, the construction of node-collaborative offloading strategies faces many challenges. Existing research has used deep-reinforcement learning methods to solve the task offloading problem in complex scenarios with dynamically changing computing resources [9]. However, single-agent offloading algorithms suffer from the problems of restricted observation areas and overfitting the training environment, which leads to the algorithms not being able to comprehensively sense the changes in the whole system [10; 11]. When the system environment is changing constantly, such algorithms have difficulty guaranteeing the offloading performance. Based on the above discussion, this paper proposes the MADDPG-based timing-dependent task offloading (MADDPG-DTO) algorithm to reduce the offloading latency and improve the resource utilization rate and the offloading strategy timeliness. The main contributions of this paper are as follows:  
* Aiming at the problem of long offloading delay due to concurrent application of services by multiple users and timing dependency constraints among tasks, this paper analyzes timing dependencies based on the depth-first traversal (DFS) algorithm and integrates gated recursive units (GRUs) into a multi-agent reinforcement learning algorithm to memorize effective information about historical offloading decisions and task attributes and predicts computational resources required for subsequent tasks. This method guarantees the supply of computational resources for user demand and reduces the number of iterations of the multi-agent algorithm learning, thus reducing the offloading delay and improving the timeliness of the offloading strategy.