Video streaming is an increasingly popular way to consume media content. Adaptive video streaming is an emerging delivery technology which aims to increase user QoE and maximise connection utilisation. Many implementations naively estimate bandwidth from a one-sided client perspective, without taking into account other devices in the network. This behaviour results in unfairness and could potentially lower QoE for all clients. We propose an OpenFlow-assisted QoE Fairness Framework that aims to fairly maximise the QoE of multiple competing clients in a shared network environment. By leveraging a Software Defined Networking technology, such as OpenFlow, we provide a control plane that orchestrates this functionality. The evaluation of our approach in a home networking scenario introduces user-level fairness and network stability, and illustrates the optimisation of QoE across multiple devices in a network.

Example citation: Mu, M., Broadbent, M., Farshad, A., Hart, N., Hutchison, D., Ni, Q. and Race, N. (2016) A scalable user fairness model for adaptive video streaming over SDNassisted future networks. IEEE Journal on Selected Areas in Communications. (Accepted)It is advisable to refer to the publisher's version if you intend to cite from this work. The growing demand for online distribution of high quality and high throughput content is dominating today's Internet infrastructure. This includes both production and user-generated media. Among the myriad of media distribution mechanisms, HTTP adaptive streaming (HAS) is becoming a popular choice for multi-screen and multi-bitrate media services over heterogeneous networks. HAS applications often compete for network resources without any coordination between each other. This leads to Quality of Experience (QoE) fluctuations on delivered content, and unfairness between end users. Meanwhile, new network protocols, technologies and architectures, such as Software Defined Networking (SDN), are being developed for the future Internet. The programmability, flexibility and openness of these emerging developments can greatly assist the distribution of video over the Internet. This is driven by the increasing consumer demands and QoE requirements. This paper introduces a novel user-level fairness model UFair and its hierarchical variant UFair HA , which orchestrate HAS media streams using emerging network architectures and incorporate three fairness metrics (video quality, switching impact and cost efficiency) to achieve user-level fairness in video distribution. The UFair HA has also been implemented in a purposebuilt SDN testbed using open technologies including OpenFlow. Experimental results demonstrate the performance and feasibility of our design for video distribution over future networks. Version: Accepted version

High quality online video streaming, both live and on-demand, has become an essential part of consumers' everyday lives. The popularity of video streaming has placed a heavy burden on the network infrastructure that now has to transfer an enormous amount of data very quickly to the end-user. To further exacerbate the situation, the Video-on-Demand (VoD) distribution paradigm uses a unicast independent flow for each user request. This results in multiple duplicate flows carrying the same video assets many times end-to-end. We present OpenCache: a highly configurable, efficient and transparent in-network caching service that aims to improve the VoD distribution efficiency by caching video assets as close to the end-user as possible. OpenCache leverages Software Defined Networking to benefit last mile environments by improving network utilisation and increasing the Quality of Experience for the end-user. Our evaluation on a pan-European OpenFlow testbed uses adaptive video streaming and demonstrates that with the use of OpenCache, the external link utilisation is reduced by 100%. Furthermore the streaming application receives better quality video and observes higher throughput, lower latency and shorter start up and buffering times.

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Despite the relative maturity of the Internet, the computer networks of today are still susceptible to attack. The necessary distributed nature of networks for wide area connectivity has traditionally led to high cost and complexity in designing and implementing secure networks. With the introduction of Software Defined Networks (SDN) and Network Functions Virtualisation (NFV), there are opportunities for efficient network threat detection and protection. SDN's global view provides a means of monitoring and defence across the entire network. However, current SDN-based security systems are limited by a centralised framework that introduces significant control plane overhead, leading to the saturation of vital control links. In this paper, we introduce TENNISON, a novel distributed SDN security framework that combines the efficiency of SDN control and monitoring with the resilience and scalability of a distributed system. TENNISON offers effective and proportionate monitoring and remediation, compatibility with widely-available networking hardware, support for legacy networks, and a modular and extensible distributed design. We demonstrate the effectiveness and capabilities of the TENNISON framework through the use of four attack scenarios. These highlight multiple levels of monitoring, rapid detection and remediation, and provide a unique insight into the impact of multiple controllers on network attack detection at scale.