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A Multi-Stage Approach for Virtual Network Function Migration and Service Function Chain Reconfiguration in NFV-enabled Networks

机译:启用NFV网络中的虚拟网络功能迁移和服务功能链重新配置的多级方法

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Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within the dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF Instance (VNFI). In this paper, we assume that each deployed VNFI is used by multiple SFCs and deal with the optimal location allocation for the contemporaneous migration of VNFIs based on the actual network situation. We first formalize the VNFI migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the end-to-end delay for all affected SFCs and to guarantee network load balancing after the migration simultaneously. Then, we prove the NP-hardness of this problem and propose a multi-stage heuristic algorithm based on optimal order (MSH-OR) to solve it. Extensive evaluation shows that the proposed approach can reduce the average delay by about 16% – 25% for different scale networks while ensuring network load balancing compared with the previous algorithms.
机译:网络功能虚拟化(NFV)作为有前途的范式,通过将网络功能从专有设备分离并以软件的形式分离在公共服务器上部署它们的网络功能来加速服务部署。启用NFV的网络中的任何服务都是作为服务功能链(SFC)实现的,由一系列有序的虚拟网络功能(VNF)组成。但是,VNF迁移VNF在动态NFV的网络中更灵活的服务是要解决的关键挑战。目前的VNF迁移研究主要关注单个VNF迁移决策,而不考虑VNF实例的共享和并发迁移(VNFI)。在本文中,我们假设多个SFC使用每个部署的VNFI,并根据实际网络情况处理VNFI的同时迁移的最佳位置分配。我们首先将VNFI迁移和SFC重新配置问题正式化为数学模型,旨在最大限度地减少所有受影响的SFC的端到延迟,并在同时迁移后保证网络负载平衡。然后,我们证明了这个问题的NP硬度,并提出了一种基于最佳顺序(MSH-OR)来解决它的多级启发式算法。广泛的评估表明,该方法可以在与先前算法相比,在确保网络负载平衡的同时降低不同尺度网络的平均延迟约16% - 25%。

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