Green Offloading in Fog-Assisted IoT Systems: An Online Perspective Integrating Learning and Control

TL;DR

This paper introduces LAGO, an online learning and control scheme for energy-efficient task offloading in fog-assisted IoT systems, reducing latency while respecting energy constraints despite system uncertainties.

Contribution

It formulates the offloading problem as a CMAB with long-term constraints and proposes LAGO, integrating bandit learning and virtual queue techniques for optimal decision-making.

Findings

LAGO achieves sublinear regret bounds in latency reduction.

LAGO effectively satisfies long-term energy constraints.

Simulations confirm theoretical performance improvements.

Abstract

In fog-assisted IoT systems, it is a common practice to offload tasks from IoT devices to their nearby fog nodes to reduce task processing latencies and energy consumptions. However, the design of online energy-efficient scheme is still an open problem because of various uncertainties in system dynamics such as processing capacities and transmission rates. Moreover, the decision-making process is constrained by resource limits on fog nodes and IoT devices, making the design even more complicated. In this paper, we formulate such a task offloading problem with unknown system dynamics as a combinatorial multi-armed bandit (CMAB) problem with long-term constraints on time-averaged energy consumptions. Through an effective integration of online learning and online control, we propose a \textit{Learning-Aided Green Offloading} (LAGO) scheme. In LAGO, we employ bandit learning methods to handle the exploitation-exploration tradeoff and utilize virtual queue techniques to deal with the long-term constraints. Our theoretical analysis shows that LAGO can reduce the average task latency with a tunable sublinear regret bound over a finite time horizon and satisfy the long-term time-averaged energy constraints. We conduct extensive simulations to verify such theoretical results.