Online Task Scheduling for Fog Computing with Multi-Resource Fairness

TL;DR

This paper introduces FairTS, a deep reinforcement learning-based online task scheduling scheme for fog computing that ensures multi-resource fairness and reduces task slowdown, outperforming existing methods.

Contribution

The paper proposes FairTS, a novel DRL-based online scheduling scheme that incorporates multi-resource fairness using dominant resource fairness principles.

Findings

FairTS achieves lower average task slowdown.

FairTS provides better resource fairness.

Simulation results outperform state-of-the-art schemes.

Abstract

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.