CogNet will develop solutions to provide a higher and more intelligent level of automated monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of 5G.

The project will conduct and exploit leading research in the areas of data gathering, machine learning, data analytics and autonomic network management. The ultimate objective is to enable the larger and more dynamic network topologies necessary in 5G, improve the end-user QoS, and to lower capital and operational costs through improved efficiencies and the use of node, link and function virtualisation.

CogNet is a complex multinational, project with a balance of industrial, research and academic partners—from the telecoms provider, equipment vendor and research domains related to machine learning, next generation networks, cloud computing and data analytics.

The consortium has considerable management experience with the EU’s Framework Programme. The coordinator, TSSG, will take best practices from previous projects, such as “PERSIST” (€5M STREP, which gained a result of “excellent” in its final review, and “SOCIETIES” (€15M, IP, which received significant praise from its external reviewers based on its various reviews.

A dedicated Quality Manager role is integrated in the project coordination structure, with this role including Risk & Contingency Management and

structured monitoring of project progress against a predefined set of risk items. To ensure efficiency, the Quality Manager will also have responsibility for Innovation Management activities in the project to ensure a consistent focus not only on production of quality outputs but on production of innovations that are ready for exploitation.

Deliverables and milestones at key points in the project will ensure CogNet’s work progresses in an iterative and timely fashion. Regular and formal contact with the European Commission will also be undertaken during the project lifecycle.

Objective 1: Research and develop a system of data collection from network nodes that involves pre-processing data to allow the node classify the data it generates and identify the most important and irregular data for submission to network management while filtering routine and regular data. This is an important step in the development of scalable network management as it dramatically reduces the scale of data required to be processed centrally.

Objective 2: While working on the principles of a self organising network, research and develop, within existing policy management frameworks, a system to allow network nodes to self-manage based on their available data while escalating higher importance issues to central network management.

Objective 3: Apply Machine Learning algorithms to develop a system of service demand prediction and provisioning which allows the network to resize and resource itself, using virtualisation, to serve predicted demand according to parameters such as location, time and specific service demand from specific users or user groups. This is achieved while optimising performance and use of available network and VM resources while minimising overall energy requirements and costs.

Objective 4: Apply Machine Learning algorithms to address network resilience issues. This includes using Supervised ML to identify network errors, faults or conditions such as congestion at both a network wide and a local level and automatically taking mitigating actions to minimise overall impact.

Objective 5: Use anomaly detection algorithms to identify serious security issues such as unauthorised intrusion or fraud and liaise with autonomic network management & policies to formulate and take appropriate action.

Objective 6: Develop a number of demonstrable applications using real-world data gathered via current 4G network nodes which demonstrate the core project innovations, and serve to highlight the exploitation potential of CogNet. The applications will include tests to demonstrate the potential improved performance and capacity that can be achieved by utilising the CogNet algorithms over conventional approaches used in today’s Network Management Systems.

Project began on 1-July, 2015

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