The development of mobile services has impacted a variety of computation-intensive and time-sensitive applications, such as recommendation systems and daily payment methods. However, computing task competition involving limited resources increases the task processing latency and energy consumption of mobile devices, as well as time constraints. Mobile edge computing (MEC) has been widely used to address these problems. However, there are limitations to existing methods used during computation offloading. On the one hand, they focus on independent tasks rather than dependent tasks. The challenges of task dependency in the real world, especially task segmentation and integration, remain to be addressed. On the other hand, the multiuser scenarios related to resource allocation and the mutex access problem must be considered. In this paper, we propose a novel offloading approach, Com-DDPG, for MEC using multiagent reinforcement learning to enhance the offloading performance. First, we discuss the task dependency model, task priority model, energy consumption model, and average latency from the perspective of server clusters and multidependence on mobile tasks. Our method based on these models is introduced to formalize communication behavior among multiple agents; then, reinforcement learning is executed as an offloading strategy to obtain the results. Because of the incomplete state information, long short-term memory (LSTM) is employed as a decision-making tool to assess the internal state. Moreover, to optimize and support effective action, we consider using a bidirectional recurrent neural network (BRNN) to learn and enhance features obtained from agents' communication. Finally, we simulate experiments on the Alibaba cluster dataset. The results show that our method is better than other baselines in terms of energy consumption, load status and latency.
翻译:移动服务的发展影响到了各种计算密集和时间敏感的应用程序,例如建议系统和日常支付方法。然而,涉及有限资源的计算任务竞争增加了移动设备的任务处理延迟度和能源消耗以及时间限制。移动边缘计算(MEC)被广泛用于解决这些问题。但是,在计算卸载时使用的现有方法存在局限性。一方面,它们侧重于独立任务而不是依赖性任务。现实世界中任务依赖性的挑战,特别是任务分割和整合,仍有待解决。另一方面,必须审议与资源分配和静电存存利问题有关的多用户设想方案。在本文中,我们提议为MEC采用新型的卸载方法,即Com-DDPG,使用多剂强化学习来提高卸载性业绩。首先,我们讨论任务依赖性模型、任务优先模型、能源消耗模型,以及从服务器集群和移动任务的多重依赖性角度看,我们根据这些模型获得的方法将多个代理商之间的通信行为正规化;然后,加强内置的内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置、内置