Building Large, Complex, Distributed Safety-Critical Operating Systems

. Safety-critical systems typically operate in unpredictable environments. Requirements for safety and reliability are in conflict with those for real-time responsiveness. Due to unpredictable environmental needs there is no static trade-off between measures to accommodate the conflicting objectives. Instead every feature or operating system service has to be adaptive. Finally, for any design problem, there cannot be any closed-form (formal) approach taking care at the same time of (external) time constraints or deadlines, and synchronization requirements in distributed design. The reason is that these two aspects are causally independent. - In this situation we worked out a heuristic experimental, performance-driven and performance-based methodology that allows in an educated way to start with a coarse system model, with accurate logical expectations regarding its behavior. Through experiments these expectations are validated. If they are found to successfully stand the tests extended..

Dezentrales autonomes Energiemanagement

The potential of adjusting the demand of certain appliances with time-flexible duty cycles is currently used for load balancing purposes only (e. g. Demand Side Management). However, a coordinated operation scheme for consumers and producers of electrical energy may also be used for grid stabilization or in general for a more efficient utilization of existing distribution grids, directly influencing line currents and voltage profiles along the network

Distributed learning strategies for collaborative agents in adaptive decentralized power systems

For regenerative electric power the traditional topdown and long-term power management is obsolete, due to the wide dispersion and high unpredictability of wind and solar based power facilities. In the R&D DEZENT1 project we developed a multi-level bottomup solution where autonomous software agents negotiate available energy quantities and needs on behalf of consumers and producer groups. We operate within very short time intervals of assumedly constant demand and supply, in our case 0.5 sec (switching delay for a light bulb). We prove security against a relevant variety of malicious attacks. In this paper the main contribution is to make the negotiation strategies themselves adaptive across periods. We adapted a Reinforcement Learning approach for defining and discussing learning strategies for collaborative autonomous agents that are clearly superior to previous (static) procedures. We report briefly on extensive comparative simulation