大规模数据中心分布式VNF扩展：一种基于ADMM的方法
Distributed VNF Scaling in Large-scale Datacenters: An ADMM-based Approach
摘要：
网络功能虚拟化（Network Functions Virtualization，NFV）是一种很有前途的网络架构，其中网络功能被虚拟化并与专有硬件解耦。在现代网络中心中，用户网络流量需要一组虚拟网络功能（VNF）作为服务链来处理流量需求。大规模数据中心的流量波动会导致服务链的过载和欠载现象。在本文中，我们提出了一种分布式方法的基础上交替方向乘法（ADMM），以联合负载平衡的流量和水平规模向上和向下的VNF在最不发达国家以最小的部署和转发成本。首先，我们制定的目标优化问题作为一个混合线性规划（MILP）模型，这是NP完全的。其次，我们把它放松成两个线性规划（LP）模型，以应付过载和欠载的服务链。在小型或中型数据中心的情况下，LP模型可以以低时间复杂度的集中方式运行。然而，在最不发达国家中，增加LP变量的数量会导致中央算法的额外时间消耗。为了缓解这一问题，我们的研究提出了一种基于ADMM的分布式方法。在不同的场景中验证了所提出的机制的有效性。
英文摘要：
Network Functions Virtualization (NFV) is a promising network architecture where network functions are virtualized and decoupled from proprietary hardware. In modern datacenters, user network traffic requires a set of Virtual Network Functions (VNFs) as a service chain to process traffic demands. Traffic fluctuations in Large-scale DataCenters (LDCs) could result in overload and underload phenomena in service chains. In this paper, we propose a distributed approach based on Alternating Direction Method of Multipliers (ADMM) to jointly load balance the traffic and horizontally scale up and down VNFs in LDCs with minimum deployment and forwarding costs. Initially we formulate the targeted optimization problem as a Mixed Integer Linear Programming (MILP) model, which is NP-complete. Secondly, we relax it into two Linear Programming (LP) models to cope with over and underloaded service chains. In the case of small or medium size datacenters, LP models could be run in a central fashion with a low time complexity. However, in LDCs, increasing the number of LP variables results in additional time consumption in the central algorithm. To mitigate this, our study proposes a distributed approach based on ADMM. The effectiveness of the proposed mechanism is validated in different scenarios.
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