  In fog computing systems, one key challenge is online task scheduling, i.e.,
to decide the resource allocation for tasks that are continuously generated
from end devices. The design is challenging because of various uncertainties
manifested in fog computing systems; e.g., tasks' resource demands remain
unknown before their actual arrivals. Recent works have applied deep
reinforcement learning (DRL) techniques to conduct online task scheduling and
improve various objectives. However, they overlook the multi-resource fairness
for different tasks, which is key to achieving fair resource sharing among
tasks but in general non-trivial to achieve. Thusly, it is still an open
problem to design an online task scheduling scheme with multi-resource
fairness. In this paper, we address the above challenges. Particularly, by
leveraging DRL techniques and adopting the idea of dominant resource fairness
(DRF), we propose FairTS, an online task scheduling scheme that learns directly
from experience to effectively shorten average task slowdown while ensuring
multi-resource fairness among tasks. Simulation results show that FairTS
outperforms state-of-the-art schemes with an ultra-low task slowdown and better
resource fairness.
