论文标题
使用不安的匪徒的缓存内容具有不同的受欢迎程度
Caching Contents with Varying Popularity using Restless Bandits
论文作者
论文摘要
移动网络正在经历数据量和用户密度的巨大增加，这给移动核心网络和回程链接带来了很大的负担。减少此问题的有效技术是使用缓存，即通过使用Edge网络节点的缓存，例如固定或移动访问点，甚至用户设备来使数据更接近用户。缓存的性能取决于缓存的内容。在本文中，我们研究了无线边缘（即基站）的内容缓存的问题，以最大程度地减少无限视野中产生的折扣成本。我们将此问题提出为不安的匪徒问题，这很难解决。我们首先显示最佳策略是阈值类型。使用这些结构性结果，我们证明了问题的索引性，并使用Whittle指数政策来最大程度地减少折扣成本。
Mobile networks are experiencing prodigious increase in data volume and user density , which exerts a great burden on mobile core networks and backhaul links. An efficient technique to lessen this problem is to use caching i.e. to bring the data closer to the users by making use of the caches of edge network nodes, such as fixed or mobile access points and even user devices. The performance of a caching depends on contents that are cached. In this paper, we examine the problem of content caching at the wireless edge(i.e. base stations) to minimize the discounted cost incurred over infinite horizon. We formulate this problem as a restless bandit problem, which is hard to solve. We begin by showing an optimal policy is of threshold type. Using these structural results, we prove the indexability of the problem, and use Whittle index policy to minimize the discounted cost.
