最小化移动边缘计算系统的信息年龄：一种嵌套索引方法
Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach
AI总结：
本文针对多用户移动边缘计算系统中的信息年龄最小化问题，将其建模为RMAB问题并建立分层MDP，提出具有渐近最优性的嵌套索引策略，实现计算复杂度与准确性的权衡，相比基准方法最优性差距最多缩小40%。
摘要：
利用移动设备和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到边缘节点，为实现对信息新鲜度敏感的实时应用提供了一种高效方法。我们使用信息年龄（AoI）指标来评估信息新鲜度。由于计算时间的随机性，为多用户MEC系统获得最小化AoI的高效解并非易事。在本文中，我们考虑多用户将任务卸载到MEC系统中的异构边缘服务器。我们首先将该问题重新表述为 restless 多臂老虎机（RMAB）问题，并建立一个分层马尔可夫决策过程（MDP）来刻画MEC系统AoI的更新过程。基于该分层MDP，我们提出了一个嵌套索引框架，并设计了一种具有可证明渐近最优性的嵌套索引策略。最后，我们得到了嵌套索引的闭式表达，从而能够在计算复杂度和准确性之间实现性能权衡。与基准方法相比，我们的算法将最优性差距缩小了最多40%。当系统标量足够大时，我们的算法渐近逼近下界。
英文摘要：
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.
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