KEYWORDS: Analytics, Network architectures, Space reconnaissance, Sensors, Information technology, Failure analysis, Databases, Data storage, Data processing, Computer architecture
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.
It is envisioned that the success of future military operations depends on the better integration, organizationally and operationally, among allies, coalition members, inter-agency partners, and so forth. However, this leads to a challenging and complex environment where the heterogeneity and dynamism in the operating environment intertwines with the evolving situational factors that affect the decision-making life cycle of the war fighter. Therefore, the users in such environments need secure, accessible, and resilient information infrastructures where policy-based mechanisms adopt the behaviours of the systems to meet end user goals. By specifying and enforcing a policy based model and framework for operations and security which accommodates heterogeneous coalitions, high levels of agility can be enabled to allow rapid assembly and restructuring of system and information resources. However, current prevalent policy models (e.g., rule based event-condition-action model and its variants) are not sufficient to deal with the highly dynamic and plausibly non-deterministic nature of these environments. Therefore, to address the above challenges, in this paper, we present a new approach for policies which enables managed systems to take more autonomic decisions regarding their operations.
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