Dynamic Flow Scheduling Strategy in Multihoming Video CDNs

TL;DR

This paper introduces a dynamic flow scheduling strategy for multihoming video CDNs that adaptively allocates resources across multiple ISPs, significantly improving performance over static methods.

Contribution

It proposes a data-driven, parameter-guided dynamic flow scheduling approach for multihoming video CDNs, addressing inefficiencies of static allocation strategies.

Findings

Achieves near-optimal performance in simulations

Demonstrates significant resource utilization improvements

Validates effectiveness through real-world setup simulations

Abstract

Multihoming for a video Content Delivery Network (CDN) allows edge peering servers to deliver video chunks through different Internet Service Providers (ISPs), to achieve an improved quality of service (QoS) for video streaming users. However, since traditional strategies for a multihoming video CDN are simply designed according to static rules, e.g., simply sending traffic via a ISP which is the same as the ISP of client, they fail to dynamically allocate resources among different ISPs over time. In this paper, we perform measurement studies to demonstrate that such static allocation mechanism is inefficient to make full utilization of multiple ISPs' resources. To address this problem, we propose a dynamic flow scheduling strategy for multihoming video CDN. The challenge is to find the control parameters that can guide the ISP selection when performing flow scheduling. Using a data-driven approach, we find factors that have a major impact on the performance improvement in the dynamic flow scheduling. We further utilize an information gain approach to generate parameter combinations that can be used to guide the flow scheduling, i.e., to determine the ISP each request should be responded by. Our evaluation results demonstrate that our design effectively performs the flow scheduling. In particular, our design yields near optimal performance in a simulation of real-world multihoming setup.