Mobile edge computing (MEC) is envisioned to integrate cloud-like capabilities into the edge of networks for improving quality of service (QoS). This makes it possible for users with resource-limited devices to execute computation-intensive tasks by offloading them to MEC nodes. Extensive works have been done for MEC. However, few of them involve user mobility. Whether to migrate task dynamically can’t be ignored when taking QoS into account. In this paper, we try to optimize task migration with user mobility consideration, in which deadlines of tasks are also involved. The problem is proved to be NP-hard. To solve it, we analyze three variants of this problem and devise a group migration (GM) algorithm with known trajectories of users. Our goal is to maximize the number of tasks whose deadlines are guaranteed. Extensive experiments are carried out, and the results confirm that GM algorithm can achieve up 35%-75% performance improvement compared three other common heuristics.

设想移动边缘计算（MEC）将类云功能集成到网络边缘，以提高服务质量（QoS）。这使得具有资源有限设备的用户可以通过将其卸载到MEC节点来执行计算密集型任务。MEC已进行了广泛的工作。但是，它们很少涉及用户移动性。考虑QoS时，不能忽略是否动态迁移任务。在本文中，我们尝试考虑用户移动性来优化任务迁移，其中还涉及任务的期限。该问题被证明是NP难的。为了解决该问题，我们分析了此问题的三个变体，并设计了具有已知用户轨迹的组迁移（GM）算法。我们的目标是最大限度地保证有期限的任务数量。