Self-aware Computing: Combining Learning and Control to Manage Complex, Dynamic Systems
Modern computing systems must meet multiple---often conflicting---goals; e.g., high-performance and low energy consumption. The current state-of-practice involves ad hoc, heuristic solutions to such system management problems that offer no formally verifiable behavior and must be rewritten or redesigned wholesale as new computing platforms and constraints evolve. In this talk, Dr. Hoffmann will discuss his research on building self-aware computing systems that address computing system goals and constraints in a fundamental way, starting with rigorous mathematical models and ending with real software and hardware implementations that have formally analyzable behavior and can be re-purposed to address new problems as they emerge.
These self-aware systems are distinguished by awareness of user goals and operating environment; they continuously monitor themselves and adapt their behavior and foundational models to ensure the goals are met despite the challenges of complexity (diverse hardware resources to be managed) and dynamics (unpredictable changes in input workload or resource availability). Dr. Hoffmann will describe how to build self-aware systems through a combination of control theoretic and machine learning techniques. He will then show how this combination enables new capabilities, like increasing system robustness, reducing application energy, and meeting latency requirements even with no prior knowledge of the application.