## 3 Preliminaries: Network Model and Problem Definition
### TOMAC-PPO Algorithm Process
tasks under the conditions of 50 users and failure probability with \(p_{en}=p_{fiber}=0.1\%\), as shown in Figure 13.

As shown in the figure, besides energy consumption, the TOMAC-PPO method can achieve the best optimization results in various evaluation indicators that are important for tasks. Due to the significant energy-saving advantages of the LC scheme, the TOMAC-PPO method falls short in optimizing energy consumption for low-priority tasks compared to LC. However, TOMAC-A2C not only focuses on the energy consumption of this type of task but also considers the optimization of the task dropout rate. If too many low-priority tasks are delegated to local processing, it may result in a high dropout rate due to weak local computing power. Therefore, TOMAC-A2C generates more energy consumption by balancing these two evaluation indicators. Except for LC, TOMAC-PPO still has the best energy optimization effect on low-priority tasks. The results demonstrate that the TOMAC-PPO scheme can more accurately respond to diverse task requirements compared to other schemes when dealing with different types of tasks. ### Experiment Results Discussion

In the above experiments, we compare the four offloading decision schemes with TOMAC-PPO at average cost, average delay, average energy consumption, and drop rate. Figure 13: Optimization effect of schemes on the key performance metrics of different types of tasks. TOMAC-PPO proposed can effectively reduce the average task cost in MEC network environments with different numbers of users and system failure probabilities. Compared to the other four baseline methods, the proposed scheme has better optimization performance in the vast majority of cases, and its advantages are particularly prominent in dealing with high-load situations. For example, the proposed TOMAC-PPO can reduce the average cost by from 19.4% to 66.6% compared to other offloading schemes under the same network load. In addition, the drop rate of some baseline algorithm with 50 users can achieve 62.5% for critical task, while the proposed TOMAC-PPO only has 5.5%. The main reasons are that (1) multi-agent parallel offloading and task allocation are introduced in the TOMAC-PPO and (2) active queue management and offloading scheduling rules have been implemented for different tasks. Certainly, the proposed TOMAC-PPO has some limitations: (1) With the continuous increase in network scale, the scalability of multi-agent systems will be challenged. The information synchronization protocol used within the system can further reduce communication overhead, and the information lag of large-scale systems is also a major issue that needs to be addressed. (2) Due to various limitations, this paper can only use Python code to implement a simulation system for simple simulation of MEC networks. In future research, professional simulation software can be further used to achieve more realistic simulations, and consideration can be given to MEC computation offloading in more scenarios such as drones and connected vehicles. ## 6 Conclusions and Further Works

Task offloading and resource allocation is a research hotspot in cloud-edge collaborative computing. This paper constructs a cloud-edge collaborative computing model, and related task queue, delay, and energy consumption model, and gives joint optimization problem modeling for task offloading and resource allocation with multiple constraints. Furthermore, it designs a decentralized task offloading and resource allocation scheme based on \"task-oriented\" multi-agent reinforcement learning. In this scheme, we present information synchronization protocol and offloading scheduling rules and use edge servers as agents to construct a multi-agent system based on the Actor-Critic framework. The proposed TOMAC-PPO applies the proximal policy optimization to the multi-agent system and combines the Transformer neural network model to realize the memory and prediction of network state information.