Cost-Aware Capacity Provisioning for Internet Video Streaming CDNs

Abstract

With the increasing popularity of the Internet video streaming services (e.g., YouTube and Netflix), content delivery networks (CDNs) are heavily used to stream video contents to users, and consume more and more power and bandwidths in recent years. In this paper, we investigate the problem of saving a video streaming CDN's operating expense, including both its energy cost and the traffic cost. From our measurement study on the CDN infrastructure of Youku, which is the largest video site in China, we find that there exists an inherent conflict between improving a video streaming CDN's energy efficiency for power saving, and maintaining the CDN's ISP-friendly server selection policy. To address this problem, we propose a cost-aware capacity provisioning algorithm, which dynamically plans the service capacities of a CDN's server clusters in numerous ISPs, and optimizes its overall operating cost regarding both the energy consumptions and the cross-ISP traffics. By using the workload derived from real-world measurement and applying actual power and bandwidth price parameters, we show with experiments that our approach can significantly reduce a video streaming CDN's overall operating cost, and avoid frequent server switches effectively. To our best knowledge, this work is the first one that identifies and resolves the inherent conflict between a CDN's energy efficiency and its ISPfriendly policy.

Understanding E-Commerce Systems under Massive Flash Crowd: Measurement, Analysis, and Implications

“…Adhikari et al [13] unveil the architecture of YouTube's video CDN in details. He and Tian [14] [15] reveal the tradeoff between energy and traffic costs of a video CDN, and propose a capacity provisioning algorithm for optimizing the overall cost. Adhikari et al [9] study the CDN selection problem in Netflix and Hulu, and present a CDN selection strategy that can significantly increase users' available bandwidth while still conforming to the business constraints.…”