The Multi-Access Edge Computing (MEC) constitutes computing over virtualized resources distributed at the edge of mobile network. For mobile users, an optimal allocation of communication and computing resources changes over time and space, and the resource allocation becomes a complex problem. Moreover, for delay constrained applications, the resource allocation to mobile users cannot be solved by approaches designed for static users, as a solution would not be obtained within a desired time. Thus, in this paper, we propose a low-complexity computing and communication resource allocation for offloading of real-time computing tasks generated with a high arrival rate by the mobile users. We exploit probabilistic modeling of the users’ movement to pre-allocate the computing resources at base stations and to select suitable communication paths between the users and the base station with the pre-allocated computing resources. The simulations show that the proposed algorithm keeps the offloading delay below 100 ms for the small tasks even with the arrival rate of five tasks per second per user, while the state-of-the-art algorithms can handle only up to 0.5 tasks per second per user. Thus, the proposal enables an exploitation of the MEC for various real-time applications even if the users are moving.

中文翻译：

---

移动用户多接入边缘计算中计算和通信资源的动态分配


多接入边缘计算（MEC）构成了对分布在移动网络边缘的虚拟化资源的计算。对于移动用户来说，通信和计算资源的最优分配随着时间和空间的变化而变化，资源分配成为一个复杂的问题。此外，对于延迟受限的应用，移动用户的资源分配不能通过为静态用户设计的方法来解决，因为无法在期望的时间内获得解决方案。因此，在本文中，我们提出了一种低复杂度的计算和通信资源分配，用于卸载移动用户以高到达率生成的实时计算任务。我们利用用户移动的概率建模来预先分配基站处的计算资源，并利用预先分配的计算资源来选择用户与基站之间合适的通信路径。模拟表明，即使每个用户每秒 5 个任务的到达率，所提出的算法也能将小任务的卸载延迟保持在 100 毫秒以下，而最先进的算法每秒只能处理最多 0.5 个任务每个用户。因此，即使用户在移动，该提案也能够将 MEC 用于各种实时应用。