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Overview of Network Management Research at Ericsson R&D Ireland

The Network Management Research Unit at Ericsson R&D Ireland comprises 2 Principal Investigators, 11 researchers (6 PhDs and 5 MScs), and 6 students (4 PhDs and 2 MScs). Established with €1.6M funding since 2001, the group focuses on identifying and developing technology components for Network Management systems to enhance Ericsson's product portfolio. Core research areas include adaptive networks, semantically rich data management, machine learning applications, and collaborative techniques for distributed computing. The unit fosters partnerships with universities, driving innovation and publications.

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Overview of Network Management Research at Ericsson R&D Ireland

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  1. Network Management Research Unit Ericsson R&D Ireland

  2. Research group overview • Headcount • 2 PIs (Prof Huw Oliver & Prof Ger Parr) • 11 Researchers (6 PhD & 5 MSc) • 6 students (4 PhD, 2 MSc) • Embark • Funding • €1.6M since 2001 (founded with IDA Capability Grant & RTI) • €1.64M for 2005-2008 (Marie Curie & RTI) • 6’th FP Calls 4&5 under review/development • Group structure • Manager, Steering Group, report to country/R&D manager & ER. • Dublin: sponsored PhDs with UCD, DCU & TCD • Athlone: Design unit collaboration & anchor

  3. Key Research Areas • Business Focus • To identify and develop technology components for Network Management systems to support the Ericsson Network Element & Network Management product portfolio. • Perspective • Massively distributed large scale adaptive networks, semantically rich data management techniques and machine learning applications which support heterogeneity and interoperability. • Scope of Activities • Evaluations, prototypes, demos, benchmarking, IPR, Innovations & Publications • Specific Interest • Peer to Peer and GRID techniques for automatically distributing computational power and data storage (4 years internal) • Policies and Ontologies for dynamic data representations (2 years internal) • Machine learning techniques to automatically correlate and discover patterns of network event data for CM. (New &Leveraged)

  4. Relationship to other research areas / research groups

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