基于协调控制器的多域SDN可扩展架构实现

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本文档探讨了一种基于协调控制器的多域SDN可扩展架构设计。软件定义网络（Software-defined Networking, SDN）是一种创新的网络架构，它将控制平面与数据平面解耦，并提供了网络编程的能力。然而，在大规模网络中，运营商通常会将其划分为多个域并使用一组控制器进行管理，这使得设计一个可扩展的SDN控制平面成为关键挑战。研究者江龙、郭楚寿、余宏虎和郭志刚来自北京邮电大学高级信息网络实验室，他们在通信工程学院，针对多供应商、多域网络环境下的SDN扩展性问题，提出了一个新颖的解决方案。他们设计了一种名为“协调控制器”的组件，其核心目标是实现不同SDN管理域之间的无缝协作。协调控制器通过统一各SDN域内使用的控制器的北向API接口，实现了跨域的协调和集成，从而提高了整个网络的灵活性和效率。为了验证这一架构的有效性，研究人员构建了一个多域SDN网络实验平台。通过实际部署和测试，他们展示了这种协调控制器如何在处理大规模、异构网络中，有效地管理和优化资源分配，同时保持良好的性能和稳定性。此外，他们的工作可能包括了对控制器间的通信协议、API标准化、以及如何处理跨域数据交换等问题的深入研究和解决。这篇研究论文不仅提供了多域SDN架构的设计思路，还可能探讨了如何在保持网络灵活性的同时，通过标准化接口和协调机制来提升整个网络的可扩展性和运营效率。这对于网络运营商和SDN技术的发展具有重要意义，为未来网络架构的演进提供了有价值的经验和参考。

A Multi-Domain SDN Scalability Architecture implementation based on the

Coordinate Controller

Jianglong Wang, Guochu Shou, Yihong Hu, Zhigang Guo

Beijing Laboratory of Advanced Information Networks

School of Information and Communication Engineering

Beijing University of Posts and Telecommunications

Beijing 100876, China

{jianglong93, gcshou, yhhu, gzgang}@bupt.edu.cn

Abstract—Software-defined Networking, known as SDN, is a

novel architecture that decouples the control plane from the

data plane, and opens a programmable network. However, it is

common practice for network operators to divide the large-

scale networks into multiple domains and manage the

networks by using a group of controllers. Designing a scalable

SDN control plane becomes a critical problem. In this paper,

we propose a SDN scalability architecture for multi-domain,

multi-vendor networking. We design and implement the

Coordinator Controller to enable different SDN administrative

domains to cooperate by unifying the northbound APIs of the

controllers which are working for different SDN domains. To

validate the proposed architecture, we build a multi-domain

experiment environment consisting of three vendors. The

results show great ability in the network state view consistency

and end-to-end provisioning services.

Keywords-SDN scalability; Coordinate Controller; multi-

vendor; multi-domain;

NTRODUCTION

With the development of the network services and web

applications, current network architecture which is

characterized by closed system does not favor network

innovation. Convergence, openness are the key features of

the future network [1]. SDN provides a programmable

network over abstracted network resources, by means of

separating the control plane from data plane and utilizing the

control plane in a centralized controller [2]. SDN is a well-

acknowledged technology supporting dynamic features of

future network and intelligent applications.

In the openflow-based SDN network, controller is used to

compute packet flow route path, generate flow tables, and

put them into the corresponding switches through openflow

protocol [3]. However, in the certain scale network, every

100 switches in the data center can produce 10000K request

per second [4], but NOX controller can only handle 30K

flow per second [5]. As the newly launched service flow

initial request is always sent to the controller, there must be a

serious problem about the response time of the new packet

flow [6]. Meanwhile, a single controller would be likely to

put the whole network at risk due to the single point of

failure [7]. Thus, as the number of SDN deployment

scenarios extends to the large scale networks, which consist

of multiple domains with different administrative vendors or

any other heterogeneous network, using multiple controllers

to manage the network becomes a common approach. The

concerns about SDN scalability are gradually introduced to a

multiple controller collaboration statement [8].

In this paper, we will have a comprehensive discussion

on the SDN scalability architecture and its latest progress.

Then, we will introduce a novel vendor-agnostic Coordinate

Controller to make SDN controllers work together. In order

to allow the Coordinate Controller to interact with SDN

controllers, we design the information should be exchanged

among domains and unify SDN domain controllers’

northbound APIs to share this information. More detailed

implementation plans are given, and we test this architecture

in multi-vendor SDN networking environment, and do the

northbound APIs (NBI) interoperability test, network state

view consistency test, end-to-end provisioning services test

over this vendor-agnostic Coordinate Controller.

II.

ACKGROUND AND RELATED WORK

A. Scalability Architecture

As it mentioned before, the SDN starts with a single

controller that would be responsible for the whole network,

such as NOX, POX. Although multi-core controller

demonstrates superior performance than traditional single

controller, it is still confronted with many barriers in its

function and performance limitation. A multi-controller

solution for control plane becomes necessary indeed [8].

Generally, SDN scalability architecture can be classified into

two types: horizontal and hierarchical structure.

Horizontal structure is that all of controllers should be in

the same level and communicate with each other in peer-to-

peer way through west-east interface. They have to share the

whole network information with each other to construct a

consistent state view. As the network topology changes, all

controllers will be synchronized its state with each other.

Thus, achieving consistence for each controller is under great

consideration, west-east interface plays an important role in

controller peers’ communication. To address this problem,

researchers propose West-East Bridge [9] for SDN networks

peering. The west-east interface can exchange the inter-

domain information among controllers and the West-East

Bridge should be compatible with different third-part

controllers and network view storage systems.

2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

DOI 10.1109/CyberC.2016.100

495

2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

DOI 10.1109/CyberC.2016.100

494

2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

DOI 10.1109/CyberC.2016.100

494

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