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Distributed VNF scaling in large-scale datacenters: An ADMM-based approach

机译：大型数据中心中的分布式VNF缩放：基于ADMM的方法

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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.
机译：网络功能虚拟化（NFV）是一种很有前途的网络体系结构，其中网络功能已虚拟化并与专有硬件分离。在现代数据中心中，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。大型数据中心（LDC）中的流量波动可能会导致服务链中出现过载和欠载现象。在本文中，我们提出了一种基于乘法器交替方向方法（ADMM）的分布式方法，以最小的部署和转发成本共同对流量进行负载均衡，并在最不发达国家中水平放大和缩小VNF。最初，我们将目标优化问题公式化为NP完全的混合整数线性规划（MILP）模型。其次，我们将其放宽为两个线性规划（LP）模型，以应对超载和欠载的服务链。在中小型数据中心的情况下，LP模型可以以较低时间复杂度的集中方式运行。但是，在LDC中，增加LP变量的数量会导致中央算法消耗更多时间。为了减轻这种情况，我们的研究提出了一种基于ADMM的分布式方法。所提出的机制的有效性在不同情况下得到了验证。

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