A Human-Augmenting Resource/Performance Co-Design Tool for Real-Time Distributed Control Systems
提出一种人机协同设计工具，通过旋钮式交互探索控制器简化与多处理器任务调度的权衡，在真实系统中以0.14%性能损失换取60%硬件成本降低，将手动设计速度提升16倍。
Designing distributed control systems (DCS) is challenging because we need to balance the application control performance with the real-time hardware resource costs. Compared to the abundance of automated tools for monolithic systems, managing the design and implementation of DCS still heavily relies on human expertise. To address this challenge, we propose a human-augmenting design tool that suggests and explores tradeoffs in co-optimizing controller simplification and multiprocessor distributed task scheduling with a simple dial. Our work creatively combines sparsity-based optimal controller simplification with task consolidation, to achieve an optimal control performance on the smallest distributed hardware footprint. Our unique task consolidator adapts to various real-time scheduling algorithms and allocates control tasks to the fewest nodes under the utilization bounds of the chosen real-time scheduler. To account for real-time guarantees when assigning the controller, we introduce an improved worst-case execution time (WCET) model based on a mixed Weibull distribution. This new WCET model provides state-of-the-art accuracy of WCET based upon fewer data samples, thus reducing design-time effort and improving run-time performance. With our tool, designers can virtually simulate the theoretical optimum, physically download it, or experimentally explore alternative tradeoffs between system cost and controller performance. In a real-world implementation, our tool reduced 60% of hardware costs by trading off merely 0.14% of control performance. By automating the “simplification – demux – WCET estimation – task consolidation” pipeline, our tool allows control engineers to balance control performance and system cost directly, speeding up manual distributed controller design by 16 times.