The non-cooperative and unsupervised resource competition between adaptive media applications (such as Youtube and Netflix) leads to significant detrimental quality fluctuations and an unbalanced share of network resources. Therefore, it is essential for content networks to better understand the application and user-level requirements of different data flows and to manage the traffic intelligently. I am glad to have been part of a team of talented researchers which was one of the first to experiment Software defined networking (SDN)-assisted QoE-aware network management using physical OpenFlow network switches. SDN is a network paradigm that decouples network control from the underlying packet forwarding. Combined with Fog Computing and Network Function Virtualization (NFV), this opens compute locations that are close to the edge to enable intelligent network traffic management services (I also name this cognitive networking).
Following the publications [1,2,3] we made, there have been numerous requests to open-source our experimentation environment (named REF – Rapid Experimentation Framework) from the research community. REF is an experimentation framework and a guide to building a testbed that together provides a blueprint for an SDN-based contextual network design facility. Contrasting to existing facilities that typically provide very detailed low-level control to just the network infrastructure, our work provides higher level abstractions of both the network and virtualisation infrastructures through orchestration, automating the creation, connection, running, and cleaning of nodes in an experiment. REF also provides abstraction over the network for making the creation of context aware traffic management applications as streamline as possible. Additionally, with the unique configuration using slicing and port multiplexing, REF can create much larger physical networks with limited hardware than its competitors. Finally, the entire REF framework can be used and modified by anyone without any kind of registration or subscription to a federation.
Needless to say, to “open-source” a framework is not a straight-forward task. Our source codes are pretty much meaningless if they are not well connected with well-configured hardware equipment and a comprehensive guideline of do’s and don’ts. We wanted to publish this tutorial-style guideline in an elite outlet (so more people can benefit from it) while keeping the writing style suitable for SDN beginners, and there is nothing more suitable for our work than the IEEE Communications Magazine. Furthermore, because we are using HPE’s network switches (3800 and later 3810 series) as reference equipment (and we know for sure that the implementation of standards (such as OpenFlow) by the vendors is a deterministic factor), we must work with HPE to make sure our analysis and conclusions are accurate. Fortunately, Bruno Hareng, an SDN and Security Solution Manager at HPE, provided invaluable input to our work.
The manuscript is set to describe the framework (shown in Figure above), covering the requirements of the framework then the purpose of each component within the system as well as the abstractions that it provides to the user. Next, the experiment testbed is detailed, providing a guide on how to construct your own virtualisation and network infrastructure for experimentation. After this, both use cases are described and used to show REF in operation, this includes a Quality of Experience (QoE)-aware resource allocation model, and a network-aware dynamic ACL. Finally, the article goes into a discussion on interesting findings that arose during the creation and use of the system. The manuscript is now accepted by IEEE Communications Magazine for publication in a July 2017 issue:
Fawcett, L., Mu, M., Hareng, B., and Race, N., “REF: Enabling Rapid Experimentation of Contextual Network Management using Software Defined Networking”, in IEEE Communications Magazine, 2017
Online video streaming is becoming a key consumer of future networks, generating high-throughput and highly dynamic traffic from large numbers of heterogeneous user devices. This places significant pressure on the underlying networks and can lead to a deterioration in performance, efficiency and fairness. To address this issue, future networks must incorporate contextual network designs that recognise application and user-level requirements. However, designs of new network traffic management components such as resource provisioning models are often tested within simulation environments which lack subtleties in how network equipment behaves in practice. This paper contributes the design and operational guidelines for a Software-Defined Networking (SDN) experimentation framework (REF), which enables rapid evaluation of contextual networking designs using real network infrastructures. Two use case studies of a Quality of Experience (QoE)-aware resource allocation model, and a network-aware dynamic ACL demonstrate the effectiveness of REF in facilitating the design and validation of SDN-assisted networking.
 Mu, M., Broadbent, M., Hart, N., Farshad, A., Race, N., Hutchison, D. and Ni, Q., “A Scalable User Fairness Model for Adaptive Video Streaming over SDN-Assisted Future Networks”, in IEEE Journal on Selected Areas in Communications. 34, 2168-2184, 2016. DOI: 10.1109/JSAC.2016.2577318
 Fawcett, L., Mu, M., Broadbent, M., Hart, N., and Race, N., SDQ: Enabling Rapid QoE Experimentation using Software Defined Networking, to appear in IFIP/IEEE International Symposium on Integrated Network Management (IEEE IM), Lisbon, Portugal, 05/2017
 Mu, M., Simpson. S., Farshad. A., Ni. Q., and Race. N., User-level Fairness Delivered: Network Resource Allocation for Adaptive Video Streaming (BEST PAPER AWARD) in Proceedings of 2015 IEEE/ACM International Symposium on Quality of Service (IWQoS), Portland, USA, 06/2015