Green Offloading in Fog-Assisted IoT Systems: An Online Perspective Integrating Learning and Control
Xin Gao, Xi Huang, Ziyu Shao, Yang Yang

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…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · IoT and Edge/Fog Computing · Age of Information Optimization
