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Many large content publishers use multiple content distribution networks to deliver their content, and many commercial systems have become available to help a broader set of content publishers to benefit from using multiple distribution networks, which we refer to as content multihoming. In this paper, we conduct the first systematic study on optimizing content multihoming, by introducing novel algorithms to optimize both performance and cost for content multihoming. In particular, we design a novel, efficient algorithm to compute assignments of content objects to content distribution networks for content publishers, considering both cost and performance. We also design a novel, lightweight client adaptation algorithm executing at individual content viewers to achieve scalable, fine-grained, fast online adaptation to optimize the quality of experience (QoE) for individual viewers. We prove the optimality of our optimization algorithms and conduct systematic, extensive evaluations, using real charging data, content viewer demands, and performance data, to demonstrate the effectiveness of our algorithms. We show that our content multihoming algorithms reduce publishing cost by up to 40%. Our client algorithm executing in browsers reduces viewer QoE degradation by 51%.

As live streaming networks grow in scale and complexity, they are becoming increasingly difficult to evaluate. Existing evaluation methods including lab/testbed testing, simulation, and theoretical modeling, lack either scale or realism. The industrial practice of gradually-rolling-out in a testing channel is lacking in controllability and protection when experimental algorithms fail, due to its passive approach. In this paper, we design a novel system called ShadowStream that introduces evaluation as a built-in capability in production Internet live streaming networks. ShadowStream introduces a simple, novel, transparent embedding of experimental live streaming algorithms to achieve safe evaluations of the algorithms during large-scale, real production live streaming, despite the possibility of large performance failures of the tested algorithms. ShadowStream also introduces transparent, scalable, distributed experiment orchestration to resolve the mismatch between desired viewer behaviors and actual production viewer behaviors, achieving experimental scenario controllability. We implement ShadowStream based on a major Internet live streaming network, build additional evaluation tools such as deterministic replay, and demonstrate the benefits of ShadowStream through extensive evaluations.