Freshness-Aware Information Update and Computation Offloading in Mobile-Edge Computing
Freshness-Aware Information Update and Computation Offloading in Mobile-Edge Computing
复制标题
移动边缘计算中的新鲜度感知信息更新和计算卸载
DOI：
10.1109/jiot.2021.3082281
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发表时间：
2021-08-15
影响因子：
10.6
通讯作者：
Wang, Shangguang
中科院分区：
文献类型：
--
作者：
Ma, Xiao;Zhou, Ao;Wang, Shangguang
移动边缘计算是一个有希望的计算范式，具有减少延迟并放松到核心网络的流量的优势。在移动边缘计算中，减少移动用户的计算卸载成本并在边缘节点维护新的信息是两个关键的，而矛盾的目标是因为两者都消耗了有限的边缘节点无线带宽。尽管已大量的努力致力于优化计算卸载决策，并且一些工作最近调查了新鲜感知的渠道分配问题，但没有先前的工作考虑过上述冲突。本文是共同优化渠道分配和计算卸载决策的第一项工作，旨在降低传感器新鲜度要求中的计算卸载成本。我们以类似于队列的发展，分析信息时代（AOI）的递归性（AOI），并将问题作为非线性整数动态优化问题。为了克服AOI计算成本折衷，AOI时间依赖性和由用户异质性引起的高复杂性的挑战，我们提出了一种算法来解决降低计算复杂性的问题。具体而言，我们首先根据Lyapunov优化技术将原始问题转换为每个时间插槽（NP-HARD）中的静态优化问题。为了降低计算复杂性，我们利用潜在游戏的有限改进属性，并进一步执行集中控制以减少改进的迭代次数。进行了模拟，结果表明所提出的算法显示出良好的有效性和可扩展性。
Mobile-edge computing is a promising computing paradigm with the advantages of reduced delay and relieved outsourcing traffic to the core network. In mobile-edge computing, reducing the computation offloading cost of mobile users and maintaining fresh information at edge nodes are two critical while conflicted objectives, as both consume the limited wireless bandwidth of edge nodes. Although extensive efforts have been devoted to optimizing computation offloading decisions and some works have investigated freshness-aware channel allocation issues recently, no prior works have considered the above conflict. This article is the first work to jointly optimize the channel allocation and computation offloading decisions, aiming at reducing the computation offloading cost within freshness requirements of sensors. We analyze the recursiveness of Age of Information (AoI) in analogy to the evolvement of a queue and formulate the problem as a nonlinear integer dynamic optimization problem. To overcome the challenges of AoI-computation cost tradeoff, AoI time dependency and high complexity caused by the heterogeneity of users, we propose an algorithm to solve the problem with reduced computation complexity. Specifically, we first transform the original problem into a static optimization problem in each time slot (which is NP-hard) based on Lyapunov optimization techniques. To reduce the computation complexity, we exploit the finite improvement property of potential games and further enforce centralized control to reduce the number of improvement iterations. Simulations have been conducted and the results demonstrate that the proposed algorithm shows good effectiveness and scalability.