Mobile edge computing (MEC) has emerged as a new paradigm to assist low latency services by enabling computation offloading at the network edge. Nevertheless, human mobility can significantly impact the offloading decision and performance in MEC networks. In this context, we propose device-to-device (D2D) cooperation based MEC to expedite the task execution of mobile user by leveraging proximity-aware task offloading. However, user mobility in such distributed architecture results in dynamic offloading decision that instigates mobility-aware task scheduling in our proposed framework. We jointly formulate task assignment and power allocation to minimize the total task execution latency by taking account of user mobility, distributed resources, tasks properties, and energy constraint of the user device. We first propose Genetic Algorithm (GA)-based evolutionary scheme to solve our formulated mixed-integer non-linear programming (MINLP) problem. Then we propose a heuristic named mobility-aware task scheduling (MATS) to obtain effective task assignment with low complexity. The extensive evaluation under realistic human mobility trajectories provides useful insights into the performance of our schemes and demonstrates that, both GA and MATS achieve better latency than other baseline schemes while satisfying the energy constraint of mobile device.

中文翻译：

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协同移动边缘计算的移动感知联合任务调度和资源分配

移动边缘计算 (MEC) 已成为一种新范式，通过在网络边缘实现计算卸载来辅助低延迟服务。然而，人员流动会显着影响 MEC 网络中的卸载决策和性能。在这种情况下，我们提出了基于设备到设备 (D2D) 合作的 MEC，通过利用邻近感知任务卸载来加速移动用户的任务执行。然而，这种分布式架构中的用户移动性会导致动态卸载决策，从而在我们提出的框架中启动移动感知任务调度。我们通过考虑用户移动性、分布式资源、任务属性和用户设备的能量约束，共同制定任务分配和功率分配，以最小化总任务执行延迟。我们首先提出基于遗传算法 (GA) 的进化方案来解决我们制定的混合整数非线性规划 (MINLP) 问题。然后我们提出了一种启发式的移动感知任务调度（MATS），以低复杂度获得有效的任务分配。在现实的人类移动轨迹下的广泛评估为我们方案的性能提供了有用的见解，并表明 GA 和 MATS 在满足移动设备能量约束的同时比其他基线方案实现了更好的延迟。