Energy-Aware Scheduling for Real-Time Multiprocessor Systems with Uncertain Task Execution Time

This paper presents an energy-aware method to schedule multiple real-time tasks in multiprocessor systems that sup-port dynamic voltage scaling (DVS). The key dierence from existing approaches is that we consider the probabilistic dis-tributions of the tasks ' execution time to partition the work-load for better energy reduction. We analyze the problem of energy-aware scheduling for multiprocessor with prob-abilistic workload information and derive its mathemati-cal formulation. As the problem is NP-hard, we present a polynomial-time heuristic method to transform the prob-lem into a probability-based load balancing problem that is then solved with worst-t decreasing bin-packing heuristic. Simulation results with synthetic, multimedia, and stereo-vision tasks show that our method saves signicantly more energy than existing methods