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 reliable services. This challenge stands as one of the most primary key factors contributing to handling dynamic and uncertain mobile environments. 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 distributed QoE-oriented computation offloading (QECO) 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 QECO. Simulation results reveal that compared to the state-of-the-art existing works, QECO increases the number of completed tasks by up to 14.4%, while simultaneously reducing task delay and energy consumption by 9.2% and 6.3%, respectively. Together, these improvements result in a significant average QoE enhancement of 37.1%. This substantial improvement is achieved by accurately accounting for user dynamics and edge server workloads when making intelligent offloading decisions. This highlights QECO's effectiveness in enhancing users' experience in MEC systems.

翻译：在移动边缘计算（MEC）领域，高效的计算任务卸载对于确保用户获得无缝的体验质量（QoE）起着关键作用。在当今互联互通的世界中，用户要求可靠的服务，保持高QoE至关重要。这一挑战是应对动态且不确定的移动环境最关键的因素之一。在本研究中，我们深入探讨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可能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），以独立最大化每个用户的长期QoE。我们提出了一种基于深度强化学习（DRL）的分布式面向QoE的计算卸载（QECO）算法，该算法使移动设备能够在无需知晓其他设备决策的情况下，自主做出卸载决策。通过数值研究，我们评估了QECO的性能。仿真结果表明，与现有最先进的工作相比，QECO将完成任务的数量提高了高达14.4%，同时分别将任务延迟和能耗降低了9.2%和6.3%。这些改进共同带来了平均QoE显著提升37.1%。这一显著改进是通过在做出智能卸载决策时，准确考虑用户动态和边缘服务器工作负载实现的。这突显了QECO在提升MEC系统中用户体验方面的有效性。