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

• URL: http://arxiv.org/abs/2008.00199v1
• Date: Sat, 1 Aug 2020 07:27:24 GMT
• Title: Green Offloading in Fog-Assisted IoT Systems: An Online Perspective Integrating Learning and Control
• Authors: Xin Gao, Xi Huang, Ziyu Shao, Yang Yang
• Abstract summary: 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. In this paper, we formulate such a task offloading problem with unknown system dynamics as a 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 textitLearning-Aided Green Offloading (LAGO) scheme.
• Score: 20.68436820937947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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