QoS management of dynamic video tasks by task splitting and skipping

Abstract

We have integrated processing with deterministic and non-deterministic resource usage in an overall application and evaluated its performance on a multi-core processor platform. The non-determinism involves image analysis, which features a high variation in computing and memory requirements, as opposed to regular stream-oriented video processing. Quality-of-Service (QoS) control is based on resource-usage estimation functions. Scalability in parallel executing sub-functions is achieved by using task skipping and splitting as a concept, as every video application can be quickly made scalable in this way. The complete framework was validated for accurate latency control of an interactive medical imaging application. The proposed QoS mechanism runs fast enough to be executed in real time, and we achieved a reduction on the latency jitter of almost 70% for average-case processing, so that the quality can be significantly improved or an inexpensive hardware system can be employed.