Online Task Scheduling for Fog Computing with Multi-Resource Fairness
This addresses the problem of fair resource allocation in fog computing for tasks with uncertain demands, but it is incremental as it builds on existing DRL and fairness methods.
The paper tackles online task scheduling in fog computing by proposing FairTS, a scheme that uses deep reinforcement learning and dominant resource fairness to shorten average task slowdown while ensuring multi-resource fairness, with simulation results showing it outperforms state-of-the-art schemes with ultra-low task slowdown and better fairness.
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.