Joint Task Partitioning and User Association for Latency Minimization in Mobile Edge Computing Networks
Joint Task Partitioning and User Association for Latency Minimization in Mobile Edge Computing Networks
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DOI：
10.1109/tvt.2021.3091458
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发表时间：
2021-08-01
影响因子：
6.8
通讯作者：
Zhang, Wenhan
中科院分区：
文献类型：
--
作者：
Feng, Mingjie;Krunz, Marwan;Zhang, Wenhan
移动边缘计算(MEC)是一种很有前途的解决方案，可以支持新兴的延迟敏感型移动应用，如自动驾驶、增强/虚拟现实和各种物联网(IoT)应用。通过在例如靠近蜂窝基站(BS)的网络边缘部署MEC服务器，可以将由这些应用生成的计算任务卸载到边缘节点(EN)并在那里快速执行。与此同时，由于预计会有大量物联网设备，分配给每个用户的通信和计算资源可能非常有限，这使得提供低延迟MEC服务变得具有挑战性。本文研究了MEC系统中的任务划分和用户关联问题，目标是最小化所有用户的平均延迟。我们假设每个任务可以被划分为多个子任务，这些子任务可以在本地设备(例如，车辆)、MEC服务器和/或云服务器上执行；每个用户可以与附近的EN之一相关联。子任务可以相互独立，也可以相互依赖。对于每种情况，我们将任务分割率和用户关联的联合优化问题描述为一个混合整数规划问题。每个问题都通过将其分解成两个子问题来解决。低层子问题是给定用户关联下的任务划分问题，可以得到最优解。更高层的子问题是用户关联，我们提出了一种基于对偶分解的方法和一种基于匹配的方法来获得近最优解。仿真结果表明，与基准方案相比，在独立子任务和依赖子任务情况下，所提方案的平均时延分别降低了约50%和40%。
Mobile edge computing (MEC) is a promising solution to support emerging delay-sensitive mobile applications, such as self-driving, augment/virtual reality, and various Internet of Things (IoT) applications. By deploying MEC servers at network edge, e.g., close to cellular base stations (BSs), the computational tasks generated by these applications can be offloaded to edge nodes (ENs) and be quickly executed there. At the same time, with the projected large number of IoT devices, the communication and computational resources allocated to each user can be quite limited, making it challenging to provide low-latency MEC services. In this paper, we investigate the problem of task partitioning and user association in an MEC system, aiming to minimize the average latency of all users. We assume that each task can be partitioned into multiple subtasks that can be executed on local devices (e.g., vehicles), MEC servers, and/or cloud servers; each user can be associated with one of the nearby ENs. The subtasks can be independent of or dependent on each other. For each case, we formulate the joint optimization of task partitioning ratios and user association as a mixed integer programming problem. Each problem is solved by decomposing it into two subproblems. The lower-level subproblem is task partitioning under a given user association, which can be solved optimally. The higher-level subproblem is user association, we propose a dual decomposition-based approach and a matching-based approach to derive near-optimal solutions. Simulation results show that compared to benchmark schemes, the proposed schemes reduce the average latency by about 50% and 40% for the cases of independent and dependent subtasks, respectively.