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Energy Consumption Analysis Satisfying Time–Energy–Probability Constraints for Modern DVFS Microprocessor

ABSTRACT Dynamic voltage and frequency scaling (DVFS) is a popular technique to save energy for modern microprocessor. DVFS-based energy-aware scheduling technique is a critical energy saving technology for multi-task execution on modern DVFS processor. Many DVFS-based energy-aware scheduling technologies are unsatisfying for the trade-off between optimizing scheduling length and saving energy, and most of these technologies do not consider task execution time with probability distribution in real world. In this paper, we propose an energy-aware probabilistic scheduling approach to balancing between optimizing scheduling length and saving energy in a time–energy–probability constrained multi-task uniprocessor system, and we consider that each task execution time follows a probability distribution. We first construct system models, including DVFS operating environment, task model with each task execution time following a probability distribution, and energy model. In order to balance between task scheduling length and energy consumption, we propose time–energy–probability constraints scheduling (TEPcs) problem for the DVFS processor system. To solve TEPcs problem, we use a probabilistic weighted timed automaton to model the running behaviours of the processor system. Then, we devise a polynomial time heuristic algorithm through the probabilistic weighted timed automaton to find the optimal energy path as the solution of TEPcs problem. A case study and repeated experimental analysis demonstrate that our method is feasible and effective.