Towards a Load-Aware Scheduling Framework for Realtime Video Cloud

A lot of video applications such as traffic jam detection and criminal tracking require quick responses for video processing, which rely on a realtime supporting framework. Compared with CPU processors, GPU acceleration can achieve high performance. However in the context of Cloud Computing, GPU-based jobs consume less CPU resources yet occupy a lot more memories compared to CPU-based jobs, especially when bottlenecks occur in CPUs or memories. In this paper, we propose a load-aware pluggable cloud framework for real-time video processing where load-aware CPU-GPU switching can be conducted at run time to alleviate the potential imbalance. We have evaluated the framework on its performance, reusability, pluggablity and scalability to show its effectiveness.