DistNSE:跨多个提供者的分布式网络服务嵌入

Ahmed Abujoda, Panagiotis Papadimitriou
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引用次数: 38

摘要

网络功能虚拟化(Network function virtualization, NFV)通过将网络功能外包给虚拟化的数据中心,并支持新的业务模式,如网络功能即服务(NFaaS),从而带来了显著的效益。在这方面,网络服务嵌入(即服务链的映射)由于需要保持正确性和满足NF位置依赖关系而提出了重大挑战。由于每个NFP的空间有限,后者需要跨多个NFP进行服务映射。由于NFPs的本地策略以及网络拓扑和资源可用性的机密性,这进一步加剧了网络服务嵌入(NSE)问题。为了解决这些问题,我们提出了DistNSE,这是一个分布式的NSE框架,可以在NSE的NFP之间进行协作,同时保持每个NFP的隐私和自主权。DistNSE通过让不同的nfp争夺服务链的不同NFs来确保竞争性定价。为此,DistNSE将NSE分解为两个步骤:(i)使用一种促进NFP之间竞争的算法对服务链进行划分;(ii)将链段分配给数据中心网络,同时确保符合NFP的政策。我们进一步将提出的嵌入方法与参与NFPs之间的互操作性和协作的通信协议结合起来。我们使用模拟来评估DistNSE的效率,并确定优于现有分布式嵌入框架(即Polyvine)的显著收益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DistNSE: Distributed network service embedding across multiple providers
Network function virtualization (NFV) brings significant benefits by outsourcing network functions (NFs) to virtualized datacenters and enabling new business models, e.g. NF as a service (NFaaS). In this respect, network service embedding (i.e., the mapping of service chains) raises significant challenges due to the need to preserve correctness and satisfy NF location dependencies. The latter creates the need for service mapping across multiple NF Providers (NFPs), given the limited footprint of each NFP. This further exacerbates the network service embedding (NSE) problem, due to the NFPs' local policies and the confidentiality of network topology and resource availability. To address these issues, we present DistNSE, a distributed NSE framework that enables the collaboration among NFPs for NSE, while maintaining the privacy and autonomy of each NFP. DistNSE ensures competitive pricing by letting different NFPs compete for different NFs of a service chain. To this end, DistNSE decomposes NSE in two steps: (i) service chain partitioning using an algorithm that fosters competition across NFPs, and (ii) the assignment of chain segments to datacenter networks, while ensuring compliance with the NFP's policy. We further couple the proposed embedding methods with a communication protocol for interoperability and collaboration among the participating NFPs. We use simulations to assess the efficiency of DistNSE and identify significant gains over an existing distributed embedding framework (i.e., Polyvine).
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