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.

中文翻译：

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移动边缘计算中的新鲜度感知信息更新和计算卸载


移动边缘计算是一种很有前途的计算范式，具有减少延迟和减轻核心网络外包流量的优点。在移动边缘计算中，降低移动用户的计算卸载成本和在边缘节点维护新鲜信息是两个关键但相互冲突的目标，因为两者都会消耗边缘节点有限的无线带宽。尽管人们在优化计算卸载决策方面付出了广泛的努力，并且一些工作最近研究了新鲜度感知通道分配问题，但之前的工作没有考虑上述冲突。本文是第一篇联合优化通道分配和计算卸载决策的工作，旨在在传感器的新鲜度要求范围内降低计算卸载成本。我们将信息时代（AoI）的递归性与队列的演化进行类比分析，并将该问题表述为非线性整数动态优化问题。为了克服 AoI 计算成本权衡、AoI 时间依赖性和用户异构性造成的高复杂性的挑战，我们提出了一种算法来解决计算复杂度降低的问题。具体来说，我们首先基于李亚普诺夫优化技术将原始问题转化为每个时隙的静态优化问题（NP-hard）。为了降低计算复杂度，我们利用潜在博弈的有限改进特性，并进一步实施集中控制以减少改进迭代的次数。进行了仿真，结果表明该算法具有良好的有效性和可扩展性。