基于nfv的混合云/雾系统中的应用组件放置

Carla Mouradian, Somayeh Kianpisheh, R. Glitho
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引用次数: 19

摘要

应用程序是一组相互作用的组件,可以按顺序、并行或使用更复杂的结构(如选择和循环)执行。因此,可以将它们建模为具有由这些构造组成的子结构的结构化图。雾计算可以通过将一些组件部署在网络边缘(即更靠近终端设备),同时将其他组件保留在云中,从而减少由远程云引起的延迟。网络功能虚拟化(Network Functions Virtualization, NFV)将软件与硬件解耦,使网络服务和应用程序能够以VNFs (Virtual Network Functions)的形式灵活部署。在NFV设置中,需要高效的放置算法将表示VNF转发图(VNF- fg)的结构化图映射到混合云/雾系统的基础设施上。迄今为止,只考虑了具有序列和平行子结构的确定性图。然而,一些现实生活中的应用程序确实需要带有子结构作为选择和循环的非确定性图。本文主要关注基于nfv的混合云/雾系统中应用组件的放置,并假设表示应用程序的图是不确定的。目标是最小化最大完工时间和成本的加权函数。该问题被建模为整数线性规划(ILP),并使用CPLEX优化工具在小规模场景下进行评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application Component Placement in NFV-based Hybrid Cloud/Fog Systems
Applications are sets of interacting components that can be executed in sequence, in parallel, or by using more complex constructs such as selections and loops. They can, therefore, be modeled as structured graphs with sub-structures consisting of these constructs. Fog computing can reduce the latency induced by distant clouds by enabling the deployment of some components at the edge of the network (i.e., closer to end-devices) while keeping others in the cloud. Network Functions Virtualization (NFV) decouples software from hardware and enables an agile deployment of network services and applications as Virtual Network Functions (VNFs). In NFV settings, efficient placement algorithms are required to map the structured graphs representing the VNF Forwarding Graphs (VNF-FGs) onto the infrastructure of the hybrid cloud/fog system. Only deterministic graphs with sequence and parallel sub-structures have been considered thus to date. However, several real-life applications do require non-deterministic graphs with sub-structures as selections and loops. This paper focuses on application component placement in NFV-based hybrid cloud/fog systems, with the assumption that the graph representing the application is non-deterministic. The objective is to minimize an aggregated weighted function of makespan and cost. The problem is modeled as an Integer Linear Programming (ILP) and evaluated over small-scale scenarios using the CPLEX optimization tool.
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