An Efficient HPU Resource Virtualization Framework for Human-Machine Computing Systems
Hui Wang, Zhiwen Yu, Zhuoli Ren, Yao Zhang, Bin Guo · 2022
Driven by state-of-the-art AI technologies, human-AI collaboration has become an important area in computer supported teamwork research. Principles for Human-Machine Computing (HMC) have been discussed to accomplish complex goals by outsourcing some computational steps to humans and collaboratively achieving more accurate results. In HMC systems, however, the human participant brings great challenges to efficient provisioning of resources. Virtualization provides ideas for increasing the agility, flexibility and scalability of resources and has been applied in traditional computer systems, such as cloud computing. Unfortunately, existing hardware virtualization scheme is not ready to address utilization, and performance limitations associated with Human Processing Unit (HPU) resources. To tackle this problem, in this paper, we propose an efficient HPU resource virtualization framework for HMC systems. In particular, we firstly describe the modeling details of the HPU resource. And on this basis, we present the Time Division Multiplexing (TDM)-based virtualization scheme which aims to establish the mapping between each real HPU (rHPU) and its virtual HPUs (vHPUs). Secondly, we apply our minds to address the vHPU reconfiguration problem by managing the vHPU waiting queue, and propose DvR-PSO algorithm. Finally, the performance of our proposed HPU virtualization framework is evaluated through simulation experiments, and the results show that our solution can make remarkable effectiveness, which can serve as guidelines for future research on HMC systems.