Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks
Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks
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DOI：
10.1109/tvt.2018.2881191
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发表时间：
2019-01-01
影响因子：
6.8
通讯作者：
Pompili, Dario
中科院分区：
文献类型：
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作者：
Tran, Tuyen X.;Pompili, Dario
移动边缘计算（MEC）是一种新兴的范例，其将云计算能力毛细分布到无线接入网络的边缘，从而使丰富的服务和应用能够靠近最终用户。在本文中，MEC使能的多小区无线网络被认为是每个基站（BS）配备了MEC服务器，协助移动的用户执行计算密集型任务，通过任务卸载。研究了联合任务卸载和资源分配问题，以最大化用户的任务卸载收益，这是衡量的任务完成时间和能源消耗的减少加权和。所考虑的问题制定为一个混合整数非线性规划（MINLP），涉及联合优化任务卸载决策，上行链路传输功率的移动的用户，并计算资源分配在MEC服务器。由于该问题的组合性质，对于大规模网络，求解最优解是困难和不切实际的。为了克服这个缺点，我们建议分解成一个资源分配（RA）问题与固定的任务卸载决定和任务卸载（TO）问题，优化相应的RA问题的最优值函数。我们使用凸和准凸优化技术解决RA问题，并提出了一种新的启发式算法的TO问题，实现了在多项式时间的次优解。仿真结果表明，我们的算法性能接近最优解，它显着提高了用户的卸载效用比传统的方法。
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.