# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
## 2 Related Works
### Timing-Dependent Task Offloading in MEC  
Mobile applications generally contain multiple delay-sensitive and computation-intensive timing-dependent tasks which need to follow a strict logical order when being executed. However, many studies mainly design offloading schemes according to the computing resource required for tasks, ignoring the timing dependencies among the subtasks. This offloading approach, which does not consider the execution order, may affect the QoE of users and aggravate the response delay. Especially in multi-user concurrent scenarios, tasks processed by different user requests may affect each other, further aggravating the complexity of task scheduling and resource allocation. The authors in [12] jointly optimized task dependencies through deadline constraints for delay-sensitive tasks and proposed an edge-computing task optimization strategy based on a directed cyclic graph model and on priority-aware scheduling to achieve the efficient processing of tasks, but they did not consider the impact of resource conflicts and resource constraints on task offloading in multi-user concurrency scenarios. The authors in [13] used the DRL algorithm to train the system to optimize energy efficiency and reduce the time to complete the task, but their use of a single-agent algorithm that only considered its own interests is not applicable to multi-user concurrent scenarios and was prone to training overfitting conditions. The authors in [14] proposed a collaborative scheme for sharing workloads among UAVs to minimize average user latency by jointly controlling task-dependent offloading decisions and allocating UAV communication resources. The authors in [15] proposed a dependency-aware task offloading framework, COFE, where users could offload computationally intensive tasks with dependency constraints to the MEC-cloud system to minimize the average latency of dependent tasks. None of the works in [14; 15] considered the problem of the uneven allocation of computational resources affecting offloading efficiency in multi-user scenarios. The authors in [16] proposed COFE, a dependency-aware task offloading framework that allowed users to offload computation-intensive tasks with dependency constraints to the MEC-cloud system. The authors in [17] proposed a deadline-violation minimization computational-offloading scheme based on task migration and merging, which effectively reduced the system's deadline violation rate in an MEC system by means of a multi-priority task ordering algorithm and a deep deterministic policy gradient (DDPG) learning algorithm, but the work did not consider resource allocation among tasks, and the single-agent algorithm used was not applicable to multi-user concurrent offloading environments. Although all of the above works optimize the task offloading method and improve the task scheduling efficiency to a certain extent, they mainly focus on considering the offloading decision of a single user per unit of time while relatively neglecting the demand of different users on computing nodes in the multi-user concurrent scenarios, and most of them are not applicable to complex offloading scenarios in which the resources of the multi-user concurrently change dynamically. The authors in [18] considered the joint optimization of topology reconstruction and sub-task scheduling to minimize the average completion time of subtasks, but the dynamic allocation of resources was not sufficiently taken into account in this work, which is a little bit less relevant to the actual scenarios. In order to ensure the global computational resource utilization in multi-user concurrent scenarios, the authors in [19] modeled the task offloading decision as a Markov decision process and applied a graph attention network to extract the dependency information of differenttasks while combining Long Short-Term Memory (LSTM) and a Deep Q-learning Network (DQN) to deal with sequential problems.