In the realm of mobile edge computing (MEC), efficient computation task offloading plays a pivotal role in ensuring a seamless quality of experience (QoE) for users. Maintaining a high QoE is paramount in today's interconnected world, where users demand responsive and reliable services. This challenge stands as one of the most primary key factors contributing to handling dynamic and uncertain mobile environment. In this study, we delve into computation offloading in MEC systems, where strict task processing deadlines and energy constraints can adversely affect the system performance. We formulate the computation task offloading problem as a Markov decision process (MDP) to maximize the long-term QoE of each user individually. We propose a decentralized QoE-oriented computation offloading (QOCO) algorithm based on deep reinforcement learning (DRL) that empowers mobile devices to make their offloading decisions without requiring knowledge of decisions made by other devices. Through numerical studies, we evaluate the performance of QOCO. Simulation results validate that the QOCO algorithm efficiently exploits the computational resources of edge nodes. Consequently, it can complete 14% more tasks and reduce task delay and energy consumption by 9% and 6%, respectively. These together contribute to a significant improvement of at least 37% in average QoE compared to an existing algorithm.

翻译：在移动边缘计算（MEC）领域中，高效的计算任务卸载在确保用户获得无缝体验质量（QoE）方面发挥着关键作用。在当今互联世界中，用户追求响应迅速且可靠的服务，维持高QoE至关重要。这一挑战是应对动态且不确定移动环境的最主要关键因素之一。本研究深入探讨MEC系统中的计算卸载问题，其中严格的任务处理截止时间和能量约束会对系统性能产生不利影响。我们将计算任务卸载问题建模为马尔可夫决策过程（MDP），以分别最大化每个用户的长期QoE。我们提出一种基于深度强化学习（DRL）的分散式QoE导向计算卸载（QOCO）算法，该算法使移动设备能够在不了解其他设备决策信息的情况下做出自己的卸载决策。通过数值研究，我们评估了QOCO的性能。仿真结果验证了QOCO算法能有效利用边缘节点的计算资源。因此，它能够多完成14%的任务，并分别降低9%的任务时延和6%的能量消耗。这些改进共同使平均QoE比现有算法至少提升37%。