Reinforcement and transfer learning for distributed analytics in fragmented software defined coalitions

Abstract By extending the Software Defined Networking (SDN), the Distributed Analytics and Information Sciences International Technology Alliance (DAIS ITA) 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.
  • Ziyao Zhang (Imperial)
  • Anand Mudgerikar (Purdue)
  • Ankush Singla (Purdue)
  • Kin Leung (Imperial)
  • Elisa Bertino (Purdue)
  • Dinesh Verma (IBM US)
  • Kevin Chan (ARL)
  • John Melrose (Dstl)
  • Jeremy Tucker (Dstl)
Date Apr-2021
Venue Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, SPIE DCS, 2021