Toward state-aware scheduling of machine-learning workloads

In cyber-physical systems (CPS), it is imperative that tasks are completed within hard deadlines and with some degree of accuracy. Traditional real-time scheduling techniques are concerned with meeting deadlines, but do not concern themselves with the functional performance of tasks. However, when tasks are comprised of machine learning (ML) models, their performance is dependent on the system or environmental state, which can be inferred through the results of other ML tasks in the task set. Additionally, with ML tasks, a functional goal can be achieved by selecting a model among several candidates with different non-functional characteristics. Dynamically selecting the best model to execute given a state estimate may therefore affect the schedulability of other system tasks. We define a task whose functional performance is correlated with the results of another task as logically dependent and propose scheduling task sets composed of ML models with these dependencies in mind to maximize functional performance. We present several examples to show the potential usefulness in CPS and tie this concept with existing literature on real-time scheduling.