# Multi-tier Caching Analysis in CDN-based Over-the-top Video Streaming Systems

## TL;DR

This paper models multi-tier CDN caching for video streaming, analyzing various design factors to minimize stall duration tail probability, and proposes an efficient algorithm validated through cloud system experiments.

## Contribution

It introduces a comprehensive model for multi-tier CDN caching in video streaming and develops a novel algorithm to optimize performance metrics considering multiple system design factors.

## Key findings

- The proposed algorithm significantly reduces stall duration tail probability.
- Theoretical bounds for SDTP are established.
- Implementation results show improved QoE in cloud environments.

## Abstract

Internet video traffic has been been rapidly increasing and is further expected to increase with the emerging 5G applications such as higher definition videos, IoT and augmented/virtual reality applications. As end-users consume video in massive amounts and in an increasing number of ways, the content distribution network (CDN) should be efficiently managed to improve the system efficiency. The streaming service can include multiple caching tiers, at the distributed servers and the edge routers, and efficient content management at these locations affect the quality of experience (QoE) of the end users. In this paper, we propose a model for video streaming systems, typically composed of a centralized origin server, several CDN sites, and edge-caches located closer to the end user. We comprehensively consider different systems design factors including the limited caching space at the CDN sites, allocation of CDN for a video request, choice of different ports (or paths) from the CDN and the central storage, bandwidth allocation, the edge-cache capacity, and the caching policy. We focus on minimizing a performance metric, stall duration tail probability (SDTP), and present a novel and efficient algorithm accounting for the multiple design flexibilities. The theoretical bounds with respect to the SDTP metric are also analyzed and presented. The implementation on a virtualized cloud system managed by Openstack demonstrate that the proposed algorithms can significantly improve the SDTP metric, compared to the baseline strategies.