Application Components Migration in NFV-based Hybrid Cloud/Fog Systems

Seyedeh Negar Afrasiabi, Somayeh Kianpisheh, Carla Mouradian, R. Glitho, A. Moghe
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引用次数: 5

Abstract

Fog computing extends the cloud to the edge of the network, close to the end-users enabling the deployment of some application component in the fog while others in the cloud. Network Functions Virtualization (NFV) decouples the network functions from the underlying hardware. In NFV settings, application components can be implemented as sets of Virtual Network Functions (VNFs) chained in specific order representing VNF-Forwarding Graphs (VNF-FG). Many studies have been carried out to map the VNF-FGs to cloud systems. However, in hybrid cloud/fog systems, an additional challenge arises. The mobility of fog nodes may cause high latency as the distance between the end-users and the nodes hosting the components increases. This may not be tolerable for some applications. In such cases, a prominent solution is to migrate application components to a closer fog node. This paper focuses on application component migration in NFV-based hybrid cloud/fog systems. The objective is to minimize the aggregated makespan of the applications. The problem is modeled mathematically, and a heuristic is proposed to find the sub-optimal solution in an acceptable time. The heuristic aims at finding the optimal fog node in each time-slot considering a pre-knowledge of the mobility models of the fog nodes. The experiment’s results show that our proposed solution improves the makespan and the number of migrations compared to random migration and No-migration.
基于nfv的混合云/雾系统中的应用组件迁移
雾计算将云扩展到网络边缘,靠近最终用户,从而允许在雾中部署一些应用程序组件,而在云中部署其他应用程序组件。网络功能虚拟化(NFV)将网络功能与底层硬件解耦。在NFV设置中,应用组件可以被实现为一组VNFs (Virtual Network Functions),这些VNFs (Virtual Network Functions)按照特定的顺序链接在一起,表示VNF-FG (VNF-Forwarding Graphs)。为了将vnf - fg映射到云系统,已经进行了许多研究。然而,在混合云/雾系统中,出现了一个额外的挑战。随着终端用户和承载组件的节点之间的距离增加,雾节点的移动性可能会导致高延迟。对于某些应用程序,这可能是不可容忍的。在这种情况下,一个突出的解决方案是将应用程序组件迁移到更近的雾节点。本文主要研究基于nfv的混合云/雾系统中应用组件的迁移。目标是最小化应用程序的总完工时间。对该问题进行了数学建模,提出了在可接受时间内寻找次优解的启发式算法。启发式算法的目的是考虑到雾节点的移动模型的预先知识,在每个时隙中找到最优的雾节点。实验结果表明,与随机迁移和不迁移相比,我们提出的迁移方案提高了最大迁移时间和迁移次数。
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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