Resource usage prediction for groups of dynamic image-processing tasks using Markov modeling

With the introduction of dynamic image processing, such as in image analysis, the computational complexity has become data dependent and memory usage irregular. Therefore, the possibility of runtime estimation of resource usage would be highly attractive and would enable quality-of-service (QoS) control for dynamic image-processing applications with shared resources. A possible solution to this problem is to characterize the application execution using model descriptions of the resource usage. In this paper, we attempt to predict resource usage for groups of dynamic image-processing tasks based on Markov-chain modeling. As a typical application, we explore a medical imaging application to enhance a wire mesh tube (stent) under X-ray fluoroscopy imaging during angioplasty. Simulations show that Markov modeling can be successfully applied to describe the resource usage function even if the flow graph dynamically switches between groups of tasks. For the evaluated sequences, an average prediction accuracy of 97% is reached with sporadic excursions of the prediction error up to 20-30%