Joint Task Assignment and Wireless Resource Allocation for Cooperative Mobile-Edge Computing
Joint Task Assignment and Wireless Resource Allocation for Cooperative Mobile-Edge Computing
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DOI：
10.1109/icc.2018.8422777
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
Hong Xing;Liang Liu;Jie Xu;A. Nallanathan
中科院分区：
文献类型：
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作者：
Hong Xing;Liang Liu;Jie Xu;A. Nallanathan
本文研究了一种多用户协作移动边缘计算（MEC）系统，其中本地移动用户可以将密集的计算任务卸载到多个附近的边缘设备，作为远程执行的助手。我们关注本地用户有多个独立任务可以并行执行但不能进一步分区的场景。我们考虑时分多址（TDMA）通信协议，其中本地用户可以将计算任务卸载给助手，并在预先安排的时隙上从它们下载结果。在此设置下，我们通过结合时间和功率分配优化任务分配来最小化本地用户的计算延迟，并受到本地用户和助手的个人能量限制。然而，联合任务分配和无线资源分配问题是一个很难最优求解的混合整数非线性规划（MINLP）。为了应对这一挑战，我们首先将其松弛为凸问题，然后基于松弛凸问题的最优解提出有效的次优解。最后，数值结果表明，与其他将任务分配与卸载/下载资源分配和本地执行分开设计的基准方案相比，我们提出的联合设计显着减少了本地用户的计算延迟。
This paper studies a multi-user cooperative mobile- edge computing (MEC) system, in which a local mobile user can offload intensive computation tasks to multiple nearby edge devices serving as helpers for remote execution. We focus on the scenario where the local user has a number of independent tasks that can be executed in parallel but cannot be further partitioned. We consider a time division multiple access (TDMA) communication protocol, in which the local user can offload computation tasks to the helpers and download results from them over pre- scheduled time slots. Under this setup, we minimize the local user's computation latency by optimizing the task assignment jointly with the time and power allocations, subject to individual energy constraints at the local user and the helpers. However, the joint task assignment and wireless resource allocation problem is a mixed-integer non-linear program (MINLP) that is hard to solve optimally. To tackle this challenge, we first relax it into a convex problem, and then propose an efficient suboptimal solution based on the optimal solution to the relaxed convex problem. Finally, numerical results show that our proposed joint design significantly reduces the local user's computation latency, as compared against other benchmark schemes that design the task assignment separately from the offloading/downloading resource allocations and local execution.