Exploiting the computational heterogeneity of mobile devices and edge nodes, mobile edge computation (MEC) provides an efficient approach to achieving real-time applications that are sensitive to information freshness, by offloading tasks from mobile devices to edge nodes. We use the metric Age-of-Information (AoI) to evaluate information freshness. An efficient solution to minimize the AoI for the MEC system with multiple users is non-trivial to obtain due to the random computing time. In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in a MEC system. We first reformulate the problem as a Restless Multi-Arm-Bandit (RMAB) problem and establish a hierarchical Markov Decision Process (MDP) to characterize the updating of AoI for the MEC system. Based on the hierarchical MDP, we propose a nested index framework and design a nested index policy with provably asymptotic optimality. Finally, the closed form of the nested index is obtained, which enables the performance tradeoffs between computation complexity and accuracy. Our algorithm leads to an optimality gap reduction of up to 40%, compared to benchmarks. Our algorithm asymptotically approximates the lower bound as the system scalar gets large enough.

翻译：利用移动设备与边缘节点的计算异构性，移动边缘计算（MEC）通过将任务从移动设备卸载至边缘节点，为信息新鲜度敏感型实时应用提供了高效实现途径。我们采用信息年龄（Age-of-Information, AoI）度量信息新鲜度。由于计算时间的随机性，针对多用户MEC系统获取AoI最小化的高效解并非易事。本文考虑多用户在MEC系统中向异构边缘服务器卸载任务的场景。首先将问题重构为休止多臂赌博机（RMAB）问题，并建立层次马尔可夫决策过程（MDP）以刻画MEC系统中AoI的更新机制。基于层次MDP，我们提出嵌套索引框架并设计具有可证渐近最优性的嵌套索引策略。最终推导出嵌套索引的闭合表达式，实现了计算复杂度与精确度之间的性能权衡。相较于基准方案，本算法可将最优性差距缩小40%。当系统规模足够大时，本算法可渐近逼近理论下界。