以服务器为中心的缓存优化调度：可追溯性与计算
Optimal Scheduling of Age-centric Caching: Tractability and Computation
摘要：
信息时代（AoI）的概念已经成为网络和控制系统中的一个重要性能指标。由AoI表示的信息新鲜度自然出现在缓存的上下文中。我们解决的内容大小不同的时隙系统的缓存更新的最佳调度。缓存和内容更新的容量有限。每个内容与在AoI中单调递减的效用函数相关联。对于这个组合优化问题，我们提出了以下贡献。首先，我们提供解决问题易处理性边界的理论结果。特别是，通过使用网络流的重新表述，我们证明了边界基本上是由内容是否大小相等决定的。其次，我们推导出一个整数线性制定的问题，其中的最优解可以获得小规模的情况下。接下来，通过数学重新表述，我们得到了一个可扩展的优化算法，使用重复的列生成。此外，该算法计算的边界的全局最优，可以用来评估任何调度解决方案的性能。大规模的情况下的性能评估证明了该算法的优势相比，贪婪的时间表。最后，我们扩展了我们的工作的适用性，循环调度。
英文摘要：
The notion of age of information (AoI) has become an important performance metric in network and control systems. Information freshness, represented by AoI, naturally arises in the context of caching. We address optimal scheduling of cache updates for a time-slotted system where the contents vary in size. There is limited capacity for the cache and for making content updates. Each content is associated with a utility function that is monotonically decreasing in the AoI. For this combinatorial optimization problem, we present the following contributions. First, we provide theoretical results settling the boundary of problem tractability. In particular, by a reformulation using network flows, we prove the boundary is essentially determined by whether or not the contents are of equal size. Second, we derive an integer linear formulation for the problem, of which the optimal solution can be obtained for small-scale scenarios. Next, via a mathematical reformulation, we derive a scalable optimization algorithm using repeated column generation. In addition, the algorithm computes a bound of global optimum, that can be used to assess the performance of any scheduling solution. Performance evaluation of large-scale scenarios demonstrates the strengths of the algorithm in comparison to a greedy schedule. Finally, we extend the applicability of our work to cyclic scheduling.
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