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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks
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Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks

机译：多服务器移动边缘计算网络的联合任务分载和资源分配

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摘要

Mobile-edge computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this paper, an MEC enabled multi-cell wireless network is considered where each base station (BS) is equipped with a MEC server that assists mobile users in executing computation-intensive tasks via task offloading. The problem of joint task offloading and resource allocation is studied in order to maximize the users' task offloading gains, which is measured by a weighted sum of reductions in task completion time and energy consumption. The considered problem is formulated as a mixed integer nonlinear program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the combinatorial nature of this problem, solving for optimal solution is difficult and impractical for a large-scale network. To overcome this drawback, we propose to decompose the original problem into a resource allocation (RA) problem with fixed task offloading decision and a task offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem. We address the RA problem using convex and quasi-convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users' offloading utility over traditional approaches.
机译：移动边缘计算（MEC）是一种新兴的范例，它向无线访问网络的边缘提供了云计算功能的毛细分布，从而使丰富的服务和应用程序与最终用户紧密相邻。在本文中，考虑了启用了MEC的多小区无线网络，其中每个基站（BS）都配备了MEC服务器，该服务器可帮助移动用户通过任务卸载来执行计算密集型任务。为了使用户的任务卸载收益最大化，研究了联合任务卸载和资源分配的问题，该任务通过任务完成时间和能耗减少的加权总和来衡量。所考虑的问题被表述为混合整数非线性程序（MINLP），该程序涉及共同优化任务卸载决策，移动用户的上行链路传输功率以及在MEC服务器上计算资源分配。由于此问题的组合性质，对于大规模网络而言，寻求最佳解决方案是困难且不切实际的。为了克服此缺点，我们建议将原始问题分解为具有固定任务卸载决策的资源分配（RA）问题和优化与RA问题相对应的最优值函数的任务卸载（TO）问题。我们使用凸和拟凸优化技术解决了RA问题，并针对TO问题提出了一种新颖的启发式算法，该算法在多项式时间内实现了次优解。仿真结果表明，我们的算法与最佳解决方案性能相近，并且与传统方法相比，该算法显着提高了用户的卸载效率。

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