Bi-Criteria Approximation for a Multi-Origin Multi-Channel Auto-Scaling Live Streaming Cloud.

Live video traffic has been widely observed to vary significantly within short timescale. In order to manage such traffic dynamic of overlay live streaming, the Content Provider (CP) may deploy a set of geo-dispersed auto-scaling servers where the pay-as-you-go deployment cost is charged by the amount of resources used due to server uploading and data transmission between servers. To support geo-distributed user demands, we study a novel multi-origin multi-channel auto-scaling live streaming cloud that pushes each channel stream in the core network overlay as a tree covering the end servers who have local demand for the channel. The Origin-to-End (O2E) delay from an origin to an end server is due to the Server-to-Server (S2S) delays of the overlay links along the path. By optimizing the overlay of the core network, we seek to minimize the deployment cost and O2E delays of the channels (i.e., a bi-criteria problem), which can be equivalently phrased as minimizing the deployment cost while meeting certain given maximum O2E delay constraints. We formulate a realistic problem capturing the major cost and delay components, and show its NP-hardness. We propose Cost-optimized Multi-Origin Multi-Channel Overlay Streaming (COCOS), a novel, efficient and near-optimal bi-criteria approximation algorithm with proven approximation ratio. Trace-driven extensive experimental results based on real-world live streaming service data validate that COCOS outperforms other state-of-the-art schemes by a wide margin (cutting the cost in general by more than 50%).