Elastic channel distribution in the cloud for live video streaming

Sammanfattning: Streaming video has strong availability requirements, while for livestreamed video low latency becomes an additional significant factor. For large-scale video streaming the streaming service must be able to scale in and out in order to conform to the interchanging demands of users. Video streaming demonstrates heavily fluctuating load, where number of viewers may increase exponentially within a few minutes. In combination with the high availability guarantees suggests that the problem is non-trivial.This thesis covers the issues of providing a cost-effective distributed live video streaming application that guarantees a seamless user experience. For instance, there are multiple channels, in the order of hundred, where each has an ever changing popularity and furthermore, users are able to watch content which was streamed for some number of hours ago. Thus, the system must both provide cached streams as well as the live-stream.In this thesis, an elasticity-providing solution for live video streaming is presented. The solution is a combination of rule-based reactive algorithm for channel distribution and a predictive method for VM instance provisioning. The results show that the algorithm, when simulating 15 channels with 80000 viewers and 50 instances, keeps underallocation of channels at less than 1% while achieving significant reduction of about 125% for channel occurrences and thereby bandwidth consumption compared to the previous channel distribution solution. As the video streaming service scales in terms of number of channels and VM instances, the reduction factor increases.