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 support dynamic voltage scaling (DVS). The key difference from existing approaches is that we consider the probabilistic distributions of the tasks´ execution time to partition the workload for better energy reduction. We analyze the problem of energy-aware scheduling for multiprocessor with probabilistic workload information and derive its mathematical formulation. As the problem is NP-hard, we present a polynomial-time heuristic method to transform the problem into a probability-based load balancing problem that is then solved with worst-fit decreasing bin-packing heuristic. Simulation results with synthetic, multimedia, and stereo- vision tasks show that our method saves significantly more energy than existing methods.