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arXiv 1906.00749cs.NI
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基于NFV的混合云/雾系统中的应用组件迁移

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

Seyedeh Negar Afrasiabi, Somayeh Kianpisheh, Carla Mouradian, Roch H. Glitho, Ashok Moghe

摘要:

雾计算将云扩展到网络的边缘,靠近最终用户,使一些应用程序组件部署在雾中,而其他组件部署在云中。网络功能虚拟化(Network Functions Virtualization,NFV)是将网络功能从底层硬件中分离出来。在NFV设置中,应用组件可以被实现为以表示VNF转发图(VNF-FG)的特定顺序链接的虚拟网络功能(VNF)的集合。已经进行了许多研究以将VNF-FG映射到云系统。然而,在混合云/雾系统中,出现了额外的挑战。雾节点的移动性可能会导致高延迟,因为最终用户和托管组件的节点之间的距离增加。这对于某些应用程序可能是不可容忍的。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/雾系统中的应用组件迁移问题。目标是最小化应用程序的总完工时间。该问题的数学模型,并提出了一种启发式算法,以找到次优解在可接受的时间。启发式的目的是找到最佳的雾节点在每个时隙考虑的雾节点的移动性模型的先验知识。实验结果表明,与随机迁移和无迁移算法相比,本文提出的算法在最大完工时间和迁移次数上都有较大的提高。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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