Heterogeneous cloud resource allocation: a case study on real-time transcoding in live streaming

The explosion in popularity of crowdsourced live streaming (CLS) has led to a huge increase in demand for cloud resources to support real-time video transcoding. CLS transcoding is real-time, geographically distributed and computationally intensive. Therefore, transcoding service providers need to cost-effectively utilize diverse heterogeneous cloud resources, while guaranteeing quality of service standards to ensure a good streaming experience for the viewers. To support the above, we developed a novel proactive-reactive resource allocation framework that optimizes the overall cost of supporting the CLS transcoding service using heterogeneous edge and cloud computing resources. The offline proactive policy evaluator aims to provide a good and adaptable resource usage plan in advance, matching the predicted demand with the heterogeneous resources. The reactive execution module monitors the actual demand online and controls the resource usage to compensate for deviations from the offline prediction. Our experiments show that the proposed approach leads to a cost reduction of 42 % compared to the fixed usage ratio strategy based on expert knowledge.