Quality-assured cloud bandwidth auto-scaling for video-on-demand applications

Abstract

There has been a recent trend that video-on-demand (VoD) providers such as Netflix are leveraging resources from cloud services for multimedia streaming. In this paper, we consider the scenario that a VoD provider can make reservations for bandwidth guarantees from cloud service providers to guarantee the streaming performance in each video channel. We propose a predictive resource auto-scaling system that dynamically books the minimum bandwidth resources from multiple data centers for the VoD provider to match its short-term demand projections. We exploit the anti-correlation between the demands of video channels for statistical multiplexing and for hedging the risk of under-provision. The optimal load direction from channels to data centers is derived with provable performance. We further provide suboptimal solutions that balance bandwidth and storage costs. The system is backed up by a demand predictor that forecasts the demand expectation, volatility and correlations based on learning. Extensive simulations are conducted driven by the workload traces from a commercial VoD system.