The importance of content delivery networks (CDN) continues to rise with the exponential increase in generation and consumption of electronic media. In order to ensure high quality of experience, CDNs often deploy cache servers that capable of storing some of the popular files close to users. Such edge caching solutions not only increase the content availability, but also result in higher download rates and lower latency. Different from the classical eviction-based algorithms, the present work formulates the content placement problem from an optimization perspective and puts forth an online algorithm for the same. In contrast to the existing optimization-based solutions, the proposed algorithm is incremental and incurs very low computation cost, while yielding storage allocations that are provably near-optimal. The proposed algorithm can handle time-varying content popularity, thereby obviating the need for periodically estimating demand distribution. Using synthetic and real IPTV data, we show that the proposed policies outperform the traditional eviction based techniques in terms of various metrics.

内容交付网络（CDN）的重要性随着电子媒体的产生和消费呈指数增长而不断提高。为了确保高质量的体验，CDN经常部署高速缓存服务器，该服务器能够在用户附近存储一些常用文件。这种边缘缓存解决方案不仅增加了内容可用性，而且还导致了更高的下载速率和更低的延迟。与经典的基于逐出的算法不同，本工作从优化角度阐述了内容放置问题，并针对该问题提出了在线算法。与现有的基于优化的解决方案相比，所提出的算法是增量式的，并且计算成本非常低，同时可证明存储分配接近最优。所提出的算法可以处理随时间变化的内容流行度，从而消除了周期性估计需求分布的需要。使用合成的和真实的IPTV数据，我们显示，在各种指标方面，拟议的策略优于基于逐出的传统技术。