arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1611.08086cs.NI
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跨地理分布式数据中心的NFV服务链的在线扩展

Online Scaling of NFV Service Chains across Geo-distributed Datacenters

Yongzheng Jia, Chuan Wu, Zongpeng Li, Franck Le, Alex Liu

摘要:

网络功能虚拟化(NFV)是一种新兴的范例,其将网络功能的依赖于硬件的实现(即,中间盒)转换为运行在虚拟化平台上的软件模块,从而显著降低成本并简化管理。这样的虚拟网络功能(VNF)通常构成业务链,以提供业务流需要经过的网络服务。用于网络服务供应的VNF的高效部署是实现NFV目标的关键。关于VNF放置的现有努力主要处理离线或一次性放置,忽略了VNF处理实际时变流量的基本动态部署和缩放需求。这项工作研究了VNF服务链在地理分布的网络中心的动态放置,以服务于分散的源和目的地对之间的流,以使服务链提供商在整个系统跨度上的运营成本最小化。提出了一种有效的在线算法,它包括两个主要部分:(1)一种基于正则化的在线学习方法,将离线最优部署问题转化为一系列单次正则化问题,每个问题在一个时隙内有效求解;(2)一种在线相关舍入方案,用于从单次问题的最优分数解导出可行整数解,并保证在线算法在整个时间跨度上的良好竞争率。我们验证了我们的在线算法与坚实的理论分析和跟踪驱动的模拟现实环境下。

英文摘要:

Network Function Virtualization (NFV) is an emerging paradigm that turns hardware-dependent implementation of network functions (i.e., middleboxes) into software modules running on virtualized platforms, for significant cost reduction and ease of management. Such virtual network functions (VNFs) commonly constitute service chains, to provide network services that traffic flows need to go through. Efficient deployment of VNFs for network service provisioning is key to realize the NFV goals. Existing efforts on VNF placement mostly deal with offline or one-time placement, ignoring the fundamental, dynamic deployment and scaling need of VNFs to handle practical time-varying traffic volumes. This work investigates dynamic placement of VNF service chains across geo-distributed datacenters to serve flows between dispersed source and destination pairs, for operational cost minimization of the service chain provider over the entire system span. An efficient online algorithm is proposed, which consists of two main components: (1) A regularization-based approach from online learning literature to convert the offline optimal deployment problem into a sequence of one-shot regularized problems, each to be efficiently solved in one time slot; (2) An online dependent rounding scheme to derive feasible integer solutions from the optimal fractional solutions of the one-shot problems, and to guarantee a good competitive ratio of the online algorithm over the entire time span. We verify our online algorithm with solid theoretical analysis and trace-driven simulations under realistic settings.

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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