Resource prediction and quality control for parallel execution of heterogeneous medical imaging tasks
We have established a novel control system for combining the parallel execution of deterministic and non-deterministic medical imaging applications on a single platform, sharing the same constrained resources. The control system aims at avoiding resource overload and ensuring throughput and latency of critical applications, by means of accurate resource-usage prediction. Our approach is based on modeling the required computation tasks, by employing a combination of weighted moving-average filtering and scenario-based Markov chains to predict the execution. Experimental validation on medical image processing shows an accuracy of 97%. As a result, the latency variation within non-deterministic analysis applications is reduced by 70% by adaptively splitting/merging of tasks. Furthermore, the parallel execution of a deterministic live-viewing application features constant throughput and latency by dynamically switching between quality modes. Interestingly, our solution can successfully be reused for alternative applications with several parallel streams, like in surveillance.