As VoD systems migrate to the Cloud, new challenges emerge in managing user Quality-of- Experience (QoE). The complexity of the cloud system due to virtualization and resource sharing complicates the QoE management. Operational failures in the Cloud could be challenging for QoE as well. We believe that end users have the best perception of system performance in terms of their QoE. We propose a QoE based adaptive control system for VoD in the Cloud. The system learns server performance from the user QoE and then adaptively selects servers for users accordingly. We deploy our proposed system in Google Cloud and evaluate it with hundreds of clients deployed all over the world. Results show that given the same amount of resources, our system provides 9% to 30% more users with QoE above the Mean Opinion Score (MOS) “good” level than the existing measurement based server selection systems. The system guarantees a better QoE (above 6% better) for 90% users. Additionally, our system discovers operational failures by monitoring QoE and prevents streaming session crashes. A computational overhead analysis shows that our system can easily scale to large VoD systems containing thousands of servers.
