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Optimal Offloading and Resource Allocation in Mobile-Edge Computing with Inter-User Task Dependency

机译:具有用户间任务依赖的移动边缘计算中的最佳卸载与资源分配

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In this paper, we consider a two-user mobile-edge computing (MEC) network, where each wireless device (WD) has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where the input of a task at one WD requires the final task output at the other WD. Under the considered task-dependency model, we study the optimal task offloading policy and resource allocation (on offloading transmit power and local CPU frequencies) that minimize the weighted sum of the WDs' energy consumption and execution time. The problem is challenging due to the combinatorial nature of the offloading decision among all tasks and the strong coupling with resource allocation among subsequent tasks. When the offloading decision is given, we obtain the closed-form expressions of the offloading transmit power and local CPU frequencies and propose an efficient method to obtain the optimal solutions. Furthermore, we prove that the optimal offloading decision follows an one-climb policy, based on which a reduced-complexity algorithm is proposed to obtain the optimal offloading decision in polynomial time. Numerical results validate the effectiveness of our proposed methods.
机译:在本文中,我们考虑了一个双用户移动边缘计算(MEC)网络,其中每个无线设备(WD)具有用于执行的任务序列。特别是,我们考虑两个WD之间的任务依赖性,其中一个WD的任务输入需要在另一个WD处的最终任务输出。在考虑的任务依赖性模型下,我们研究最佳任务卸载策略和资源分配(在卸载发射功率和本地CPU频率上),其最小化WDS的能量消耗和执行时间的加权之和。由于所有任务中的卸载决策的组合性质以及随后任务中的资源分配的强耦合,问题是具有挑战性的。当给出卸载决定时,我们获得卸载发射功率和局部CPU频率的闭合表达,并提出了一种获得最佳解决方案的有效方法。此外,我们证明了最佳的卸载决策遵循一个攀登策略,基于该策略,基于该策略,提出了一种减少复杂性算法来获得多项式时间中的最佳卸载决策。数值结果验证了我们所提出的方法的有效性。

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