Online Traffic Allocation Based on Percentile Charging for Practical CDNs

Abstract

With the explosion of data transmitted over the Internet, Content Delivery Networks (CDNs) carry massive network traffic globally and suffer an increasingly higher bandwidth cost. A critical issue for CDN service providers is how to allocate network traffic among CDN facilities to reduce the total bandwidth cost without violating the quality of service. This work studies online traffic allocation in CDNs to minimize the bandwidth cost under the 95th percentile charging model. Specifically, we here take into account practical deployment issues in large-scale CDN systems, e.g., allocation granularity and deviation. We first theoretically prove the approximation hardness of the traffic allocation problem. We then propose a novel prediction-based algorithm named OnTPC, which effectively addresses constraints raised in practical deployment. Extensive experiments demonstrate that OnTPC outperforms state-of-the-art baselines and is expected to save over a million dollars per month for our large-scale commercial CDN collaborator. Moreover, the performance of OnTPC is consistently outstanding under various settings, and specifically robust to large allocation deviation.