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12 April 2021 Reinforcement and transfer learning for distributed analytics in fragmented software defined coalitions
Ziyao Zhang, Anand Mudgerikar, Ankush Singla, Kin K. Leung, Elisa Bertino, Dinesh Verma, Kevin Chan, John Melrose, Jeremy Tucker
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Abstract
By extending the Software Defined Networking (SDN), the Distributed Analytics and Information Sciences International Technology Alliance (DAIS ITA) https://dais-ita.org/pub has introduced a new architecture called Software Defined Coalitions (SDC) to share communication, computation, storage, database, sensor and other resources among coalition forces. Reinforcement learning (RL) has been shown to be effective for managing SDC. Due to link failure or operational requirements, SDC may become fragmented and reconnected again over time. This paper shows how data and knowledge acquired in the disconnected SDC domains during fragmentation can be used via transfer learning (TL) to significantly enhance the RL after fragmentation ends. Thus, the combined RL-TL technique enables efficient management and control of SDC despite fragmentation. The technique also enhances the robustness of the SDC architecture for supporting distributed analytics services.
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Ziyao Zhang, Anand Mudgerikar, Ankush Singla, Kin K. Leung, Elisa Bertino, Dinesh Verma, Kevin Chan, John Melrose, and Jeremy Tucker "Reinforcement and transfer learning for distributed analytics in fragmented software defined coalitions", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117461W (12 April 2021); https://doi.org/10.1117/12.2587874
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KEYWORDS
Analytics

Network architectures

Computer architecture

Data processing

Data storage

Databases

Failure analysis

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