Joint Task Scheduling and Computing Resource Allocation Optimization Strategy in Asynchronous Mobile Edge Computing Networks
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摘要: 移动边缘计算(MEC)通过将密集型任务从传感器卸载到附近边缘服务器，来增强本地的计算能力，延长其电池寿命。然而，在面向无线传感器网等时变环境中，任务之间的异构性可能会导致通信低效率、高时延等问题。为此，该文提出一种异步移动边缘计算网络中的联合任务调度与计算资源分配优化策略，该策略实时感知任务信息年龄和能耗，将异步边缘卸载问题数学建模为NP难(NP-hard problem)的混合整数规划问题，并提出基于混合动作优势演员-评论家(HA2C)强化学习算法的任务调度和计算资源分配方案解决该问题。仿真结果表明，该文方法能显著降低异步卸载网络的平均信息年龄和能耗，满足无线传感器网络对任务时效性的要求。Abstract:
Objective Mobile Edge Computing (MEC) is a key technology for addressing the limited computing capabilities and energy constraints of wireless devices. MEC improves local computing performance and extends battery life by offloading computationally intensive tasks from sensors to nearby edge servers. However, in dynamic environments such as anomaly detection, environmental monitoring, and vehicle positioning, task heterogeneity becomes a significant factor limiting performance. For example, the asynchrony of task generation times can result in issues such as low communication efficiency and increased latency. Furthermore, traditional latency measurement techniques often fail to accurately assess task timeliness. To address these challenges, this paper proposes a strategy for the joint optimization of task scheduling and computational resource allocation in asynchronous MEC networks. The proposed strategy adaptively optimizes task scheduling and resource allocation, minimizing the average information age and energy consumption, thereby enhancing overall system performance. Methods This paper focuses on age-aware asynchronous MEC offloading and resource allocation. Specifically, a mathematical model is formulated based on the First Come First Served (FCFS) queuing principle, considering the order of asynchronous task arrivals. This model optimizes task scheduling and computational resource allocation in asynchronous MEC offloading, with the goal of minimizing the Average Age of Information (AoI) and average energy consumption. In dynamic asynchronous MEC, optimization problems are inherently complex. When these tasks involve both binary offloading decisions and continuous resource allocation, the combination of actions further complicates problem-solving, transforming it into a non-convex optimization challenge. Additionally, the actor network of the Actor-Critic algorithm (A2C) adapts its output layer to either a Categorical or Gaussian distribution, depending on whether the action space is discrete or continuous. This paper proposes a Hybrid Advantage Actor-Critic (HA2C) Deep Reinforcement Learning (DRL) algorithm, which effectively optimizes dynamic task scheduling and computational resource allocation strategies as tasks are generated. Results and Discussions In the simulations, the performance of the algorithm is evaluated by comparing four different strategies: random strategy, DRL strategy, delay strategy, and synchronous strategy. The following conclusions are drawn: 1. Average AoI is more sensitive to task timeliness than latency metrics. It not only accounts for the time interval between task generation and reception but also considers the intervals between task generations, offering a better measure of task timeliness. Moreover, the HA2C algorithm effectively balances the timeliness of information and energy consumption, achieving optimal average AoI and energy consumption ( Figure 4 ). 2. The hybrid action space of the HA2C algorithm is better suited for adapting to a growing number of devices. As the number of devices increases, HA2C significantly outperforms multi-agent algorithms and traditional A2C algorithms (Figure 5 ). This is because the number of actions in a discrete action space grows exponentially with the device count, ultimately leading to the curse of dimensionality, which degrades the performance of discrete DRL algorithms. 3. In the asynchronous MEC model, task generation occurs instantaneously and asynchronously. This setup allows a large amount of computational resources to be concentrated on tasks that arrive earlier, maximizing the utilization of MEC resources. As a result, asynchronous models outperform synchronous models in terms of both average AoI and average energy consumption (Figure 6 ). In conclusion, these experiments confirm that, compared to synchronous models, asynchronous models not only significantly improve computational efficiency but also effectively reduce energy consumption. Furthermore, the proposed HA2C algorithm proves to be highly effective in solving the asynchronous edge offloading and resource allocation problems, maintaining efficient performance even as the number of devices increases.Conclusions This paper leverages MEC to address the limited computational capacity and energy of wireless devices. Specifically, the paper considers scenarios where Wireless Sensor Network (WSN) edge computing systems continuously collect and process data to monitor real-time changes in the detection environment. In these contexts, the paper focuses on the heterogeneous generation times of sensor tasks deployed at different locations. The optimization goal is to minimize both average information age and energy consumption, achieved through task scheduling and adaptive resource allocation. The HA2C algorithm is designed to handle dynamic and unpredictable system changes while simultaneously managing both continuous and discrete actions. Simulation results demonstrate that the algorithm significantly reduces average information age and energy consumption in asynchronous offloading networks, while meeting the timeliness requirements of tasks in WSNs. -
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