CoupHM: Task Scheduling Using Gradient Based Optimization for Human-Machine Computing Systems

We witnessed great advancement in Artificial Intelligence (AI) powered technologies in recent years, and yet, when applied to certain high-stake contexts, such as medical diagnosis, automatic driving and criminal justice, they are not qualified. This matter can be greatly settled by Human-Machine Computing (HMC), which is an effective computing paradigm that couples the expertise and demonstration abilities of humans with the high-performance computing power of machines. This work studies an optimal task scheduling problem for HMC systems, where various tasks are decomposed and dispatched to humans and AI-enabled machines to provide significantly better benefits compared to either type of computing resources in isolation. However, designing such optimal task scheduling is challenging because of the stochastic hybrid features of machines, as well as various human professional abilities. Considering the Quality of Service (QoS) and the heterogeneity of human-machine computing resources, we propose CoupHM, a feasible task scheduler using gradient based optimization for HMC systems. In particular, we firstly present the underlying architecture of HMC system and details of the task-driven workload model. On that basis, we then formulate the objective optimization problem to be solved and describe the composition of the CoupHM scheduler. Finally, the performance of our solution is evaluated by the simulation experiments, and the results indicate that the proposed scheduler has preferable performance both in balancing resources and guaranteeing QoS, which can serve as guidelines for future research on HMC systems.