Distributed Fair Randomized (DFR): a Resource Sharing Protocol for Fog Providers

R. Beraldi, H. Alnuweiri
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Abstract

Fog computing promises to support many emerging classes of applications that can’t be rely on a cloud-only backend. Fog-to-Fog (F2F) cooperation is suggested in the openFog’s Fog computing Reference Architecture, now adopted as an IEEE standard, as a way to improve the computation service provided by this computing delivery model.In this paper, we propose DFR - Distributed Fair Randomized, a distributed F2F cooperation algorithm that allows for sharing computation resources among fog providers that agree on a (reasonable) measure of fairness. We adopt an analytical approach to study the cooperation problem of providers subject to different load conditions. We initially put the cooperation problem in the light of a simple game-theory framework to capture the selfish behavior of providers without any fairness criteria and its consequence in limiting cooperation. Then, we cast the problem as an optimization problem that incorporates fairness. Preliminary simulations results show how DFR converges to the predicted optimal value.
分布式公平随机(DFR):雾提供者的资源共享协议
雾计算承诺支持许多新兴的应用程序,这些应用程序不能只依赖云计算后端。openFog的雾计算参考架构(Fog-to-Fog, F2F)提出了雾对雾(Fog-to-Fog, F2F)协作,该架构现已被采纳为IEEE标准,作为改进该计算交付模型提供的计算服务的一种方式。在本文中,我们提出了DFR -分布式公平随机化,这是一种分布式F2F合作算法,允许在同意(合理)公平度量的雾提供者之间共享计算资源。采用分析的方法研究了不同负荷条件下供应商的合作问题。我们首先将合作问题置于一个简单的博弈论框架中,以捕捉提供者在没有任何公平标准的情况下的自私行为及其限制合作的后果。然后,我们将该问题转换为包含公平性的优化问题。初步的仿真结果表明,DFR收敛于预测的最优值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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