Serving Content with Unknown Demand:the High-Dimensional Regime

TL;DR

This paper investigates content placement in large-scale systems with many servers and content types, showing that adaptive schemes outperform traditional separation-based methods, especially when demand statistics are unknown or changing.

Contribution

It proves that separating learning and placement is suboptimal in high-dimensional content distribution, and introduces an adaptive scheme that outperforms separation-based approaches.

Findings

Separation of learning and placement is order-wise suboptimal.

Adaptive scheme based on recent requests outperforms separated schemes.

Results extend to time-varying demand statistics.

Abstract

In this paper we look at content placement in the high-dimensional regime: there are n servers, and O(n) distinct types of content. Each server can store and serve O(1) types at any given time. Demands for these content types arrive, and have to be served in an online fashion; over time, there are a total of O(n) of these demands. We consider the algorithmic task of content placement: determining which types of content should be on which server at any given time, in the setting where the demand statistics (i.e. the relative popularity of each type of content) are not known a-priori, but have to be inferred from the very demands we are trying to satisfy. This is the high- dimensional regime because this scaling (everything being O(n)) prevents consistent estimation of demand statistics; it models many modern settings where large numbers of users, servers and videos/webpages interact in this way. We characterize the performance of any scheme that separates learning and placement (i.e. which use a portion of the demands to gain some estimate of the demand statistics, and then uses the same for the remaining demands), showing it is order-wise strictly suboptimal. We then study a simple adaptive scheme - which myopically attempts to store the most recently requested content on idle servers - and show it outperforms schemes that separate learning and placement. Our results also generalize to the setting where the demand statistics change with time. Overall, our results demonstrate that separating the estimation of demand, and the subsequent use of the same, is strictly suboptimal.