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Details of Grant 

EPSRC Reference: EP/P033202/1
Title: Software Defined Cognitive Networking: Intelligent Resource Provisioning For Future Networks
Principal Investigator: Mu, Dr M
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Hewlett Packard Lancaster University
Department: School of Science and Technology
Organisation: University of Northampton
Scheme: First Grant - Revised 2009
Starts: 01 August 2017 Ends: 31 July 2019 Value (£): 99,772
EPSRC Research Topic Classifications:
Mobile Computing Networks & Distributed Systems
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
19 Apr 2017 EPSRC ICT Prioritisation Panel April 2017 Announced
Summary on Grant Application Form
The non-cooperative competition of network resources between a growing number of adaptive media applications has a significant detrimental impact on user experience and network efficiency. This can lead to knock-on effects to the digital economy and digital public services, which are increasingly dependent on high quality and reliable media streaming. Existing network infrastructures often prioritise improved network coverage and fast packet forwarding functions, which do not always effectively contribute to the improved user experience. Ultimately, the quality of user experience and network efficiency are the two of the most important benchmarks for online media distribution. Future network management must leverage application and user-level cognitive factors in order to allocate scarce network resources effectively and intelligently. this First Grant project aims at developing software defined cognitive networking (SDCN) to ensure the user experience, user-level fairness and network efficiency of online adaptive media using SDN-assisted and QoE-aware resource management. SDCN will lay the groundwork for a great leap from the conventional resource provisioning and traffic engineering schemes to context-aware network management.

In order to achieve its objective, the project will develop a cognitive model based on the analysis of human factors of adaptive media experience, iterations of subjective experiments, and data modelling. The model will enable a non-intrusive QoE assessment service that monitors adaptive media flows and estimates their perceptual user experience using a number of application, service, and network-level metrics. The model will use a purpose-built multi-objective resource allocation function to derive optimal solutions to provision available network resource for the improved user experience, fairness and network efficiency in a network segment.
Key Findings
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Summary
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