BLM-DTO:基于强盗学习和匹配的雾网络分布式任务卸载

Hoa Tran-Dang, Dongsung Kim
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引用次数: 1

摘要

本文提出了一种名为BLM-DTO的算法,该算法允许每个雾节点(FN)在雾计算网络中以分布式的方式实现任务卸载操作。从根本上说,BLM-DTO利用匹配博弈论的原理,基于博弈双方的偏好关系来实现稳定的匹配结果。由于雾计算环境的动态性,一方博弈参与者的偏好关系是先验未知的,只能通过与另一方博弈参与者的迭代交互来实现。因此,BLM-DTO进一步结合多臂强盗(MAB)学习,使用汤普森采样(TS)技术自适应学习他们的未知偏好。大量的仿真结果证明了所提出的ts型卸载算法相对于ϵ-greedy和上界置信度(UCB)型基线的潜在优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BLM-DTO: Bandit Learning and Matching based Distributed Task Offloading in Fog Networks
This paper proposes an algorithm called BLM-DTO that allows each fog node (FN) to implement the task offloading operations in a distributed manner in the fog computing networks (FCNs). Fundamentally, BLM-DTO leverages the principle of matching game theory to achieve the stable matching outcome based on preference relations of two sides of the game. Due to the dynamic nature of fog computing environment, the preference relation of one-side game players is unknown a priori and achieved only by iteratively interacting with the other side of players. Thus, BLM-DTO further incorporates multi-armed bandit (MAB) learning using Thompson sampling (TS) technique to adaptively learn their unknown preferences. Extensive simulation results demonstrate the potential advantages of the proposed TS-type offloading algorithm over the ϵ-greedy and upper-bound confidence (UCB)-type baselines.
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