Learning-based Energy Management for Cyber-Physical Systems

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With the advance of engineering and networking technology, many embedded devices are now connected into a network or with the Internet, and are emerging into cyber-physical systems. A cyber-physical system can be found in various applications for control and monitoring, such as automotive, aerospace, health care, transportation, building and process control, and entertainment. Unlike desktop systems, many cyber-physical systems operate with batteries that have limited energy supplies. Thus, energy efficiency is one of the inherent requirements of cyber-physical systems, as low-energy consumption yields better battery life, which is especially important for applications involving implanted medical devices. With more and more cyber-physical systems around us nowadays, low-energy consumption problem of cyber-physical systems becomes more critical. ******The research challenge to minimize the energy consumption of CPU or devices, while still meeting the constraints of the real-time systems, has attracted much attention in the past decade. With the variety of system configurations and task characteristics, a scheduling arrangement with Dynamic Voltage/Frequency Scaling (DVFS) and/or Dynamic Power Management (DPM) that is energy efficient for one system configuration might not be appropriate for another. Therefore, it is important to design scheduling algorithms that can be adapted to various system configurations and task characteristics.******The objective of this project is to develop adaptive efficient algorithms for scheduling co-design problems for various types of cyber-physical systems subjected to various timing and resource constraints. The plan is to adopt learning-based methods that are able to learn an implicit model for voltage selection or scheduling strategy selection for the underlying cyber-physical system based on scheduling history. This method is especially useful when the task features and architecture model are unknown to (or too complex to be considered by) the DVFS scheduler. The problem of extracting good features to serve as input for an implicit model for the learning-based method, that can best represent the model of an underlying cyber-physical system, will also be explored. Currently, Q-learning, Double Q-learning and Deep Double-Q-learning will be explored. Evaluation of the framework will also be studied extensively. The framework designed will be used to explore systems with various types of tasks (dependent, independent, periodic or non-periodic, etc.), various types of scheduling policy (earliest deadline first, fixed-priority, etc.), various types of system configurations (single-core, multi-core, GPU, NOC-based, wearable type of devices, etc.).